Project ELADIN

The Elimination of Landmines by Aqueous Detection,

Identification and Neutralization

A First Field Trial

The University of Missouri-Rolla

For the Humanitarian Demining Program

Night Vision and Electronic Sensors Directorate US Army CECOM RDEC

Ft Belvoir

September 30, 2002

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Executive Summary Waterjets at intermediate pressures have been used for many years to penetrate and remove soil. The sound that such a jet makes on impacting different target varies with the interface encountered. By using these two attributes, together with the ability of an abrasive laden jet to cut through metal and other materials, a combined package, based upon a single pump, has been proposed for finding, uncovering and neutralizing landmines... Preliminary development tests were performed in the laboratory at University Missouri Rolla (UMR) to develop an initial set of prototype equipment and analytical software. The system was then tested in the field at a government facility. The ability to locate buried objects and separate harmless and harmful objects based on the sound produced when a waterjet strikes the buried object was investigated. Three techniques of classifying the object, based on the sound were studied, based on the temporal, spectral, and a combination of temporal and spectral approaches. Methods were first developed using laboratory data at UMR and later calibration data taken at the field site. A prototype handheld waterjet probe was developed and was used to gather the field data taken in the field. Classification and identification results generated by these techniques are presented both for the laboratory and field calibration data as well as for a “blind” test lane at the test site where the presence or identity of objects was not given to the investigating team. In addition to locating the mines using audio data, the ability of the same pump to generate waterjets to remove the soil overlying a buried object and thus exposing them so that they can be identified, was demonstrated over a variety of inert mines and other objects. Abrasive was then added to the fluid stream to create a cutting jet, from the same pickup-mounted pump system. This jet was used to cut a slot measuring up to 1.5 inches deep into a piece of local steel as a demonstration of the ability of the jet to dissect any mine found. The initial tests demonstrated the flexibility of the overall system and the ability to control (potentially from a remote location) the uncovering and neutralization of the mine perform this function well and a prototype device was developed for use in the field.

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Table of Contents 1 INTRODUCTION............................................................................................................................... 4

2 DETECTION APPROACH............................................................................................................. 14 2.1 WEIGHTED DENSITY DISTRIBUTION APPLIED TO TEMPORAL ACOUSTIC DATA ......................... 14 2.2 HIDDEN MARKOV MODEL AND LINEAR PREDICTIVE COEFFICIENT APPROACH ......................... 16 2.3 MAXIMUM LIKELIHOOD APPLIED TO POWER SPECTRAL DENSITY ............................................. 23

3 DETECTION IN THE LABORATORY ........................................................................................ 27 3.1 DATA AND DATA COLLECTION................................................................................................... 27

3.1.1 UMR Sand Data Collected in 1999 ...................................................................................... 27 3.1.2 UMR Sand Data Collected in July 2002............................................................................... 30

3.2 APPLICATION AND RESULTS USING WDD APPROACH ............................................................... 32 3.2.1 UMR Sand Data Collected in 1999 ...................................................................................... 32 3.2.2 UMR Sand Data Collected in July 2002............................................................................... 35

3.3 APPLICATION AND RESULTS USING HMM APPROACH............................................................... 37

4 DETECTION IN THE FIELD......................................................................................................... 39 4.1 DATA AND DATA COLLECTION................................................................................................... 39

4.1.1 Hardware.............................................................................................................................. 39 4.1.2 Calibration Lanes ................................................................................................................. 41

4.2 APPLICATION AND RESULTS USING WDD APPROACH (CALIBRATION) ..................................... 45 4.3 APPLICATION AND RESULTS USING HMM APPROACH (CALIBRATION) ..................................... 49 4.4 APPLICATION AND RESULTS USING MAXIMUM LIKELIHOOD APPROACH (CALIBRATION) .......... 53 4.5 BLIND TEST-LANE RESULTS........................................................................................................ 56

5 IDENTIFICATION APPROACH................................................................................................... 60

6 IDENTIFICATION HARDWARE ................................................................................................. 60

7 IDENTIFICATION IN THE FIELD .............................................................................................. 67

8 NEUTRALIZATION APPROACH ................................................................................................ 71

9 NEUTRALIZATION HARDWARE............................................................................................... 71

10 NEUTRALIZATION IN THE FIELD............................................................................................ 74

11 REVIEW AND CONCLUSIONS .................................................................................................... 76

12 REFERENCES.................................................................................................................................. 78

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1 Introduction In the historic methods for finding landmines, the most consistently successful method has been where an individual has probed into the ground with a thin tool, inclined at a forward angle. The presence of a mine-like object is detected by resistance to the passage of the tool, and its size determined by a series of probing efforts, which can outline the size and possible shape of the device depending on the time taken. Once the possible mine has been located, frequently the overlying soil is gently removed by hand to expose the device. Common practice is then to explosively destroy the mine, which can, on occasion, create some collateral damage. This program looks at an alternative approach in which the mechanical manually operated probe is replaced by the pulse from a waterjet lance. Such pulses can penetrate a considerable distance into the ground at a relatively rapid rate. In many conditions the jet has penetrated to the majority of its full depth in around 0.014 seconds1, and this depth, a function of jet pressure and nozzle diameter, can extend considerably beyond 12 inches2. As the jet penetrates the soil, and then impacts on an object buried in that soil, the sound that is generated changes. Preliminary testing of this concept at University Missouri Rolla (UMR)3, showed that it was possible to discriminate between the sounds made by an inert plastic anti-personnel landmine (AP), a metal pipe, and a rock. This approach was initially adopted as a means for detecting the presence of a landmine. As the technique was studied in the laboratory, it was noted that the soil over buried mines and objects behaved in a different manner to that where no mine existed. Thus a Doppler radar device for measuring the velocity of soil movement was included in the equipment developed. Once an object has been found then the need is to gently, but rapidly and safely, remove the soil from over the device so that it can be identified and dealt with appropriately. In earlier work for the Department of Energy UMR engineers had developed a tool that has become known as the Confined Sluicing End Effector (CSEE)4. A version of the tool was used to remotely remove the high-level radioactive waste from the underground storage tanks at Oak Ridge5, and has a demonstrated capability of being remotely operated. This tool uses three waterjets that rotate around a central suction tube to clear a path for the tube through the soil, driving the soil particles into the tube, whence they are removed. The device can be operated by the pump that is also used to supply the detection equipment. In the late 1980’s6 a UMR team demonstrated that it was possible to cut into explosively laden metal containers using high-pressure waterjets laden with abrasive particles, as a means of accessing the explosive and washing it out. That technology has since been used in industrial applications for decommissioning munitions7, and is increasingly being adopted by EOD teams for dealing with unexploded ordnance (UXO)8. A tool could be built, using this approach, and making use of the same pump that is used for the detection and exposure of the mine, to create an abrasive waterjet that will cut through the mine,

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including the fuse if necessary, to either neutralize the mine or give access to the internal explosive so that it can be neutralized by other means. By combining these three steps into a single unit, a flexible tool for use in Humanitarian Demining could be developed. The first trials of this unit are described in the following pages.

1.1 Detection Hardware Over the past three decades high-pressure waterjets have been increasingly adopted as the tool of choice in cleaning surfaces. The range of applications is broad, from domestic to industrial applications, and the technology was widely used to clean military equipment returning from the Middle East after the Gulf War. As a result, relatively small (5gpm) pump units have become quite readily available at reasonable cost and with acceptable reliability. Because the pump was to be used to cut into metal casings (to a thickness of half-an-inch) as part of the neutralization phase the pump selected was one that would produce this flow at a pressure of 5,000 psi. (Initially equipment was planned to operate at pressures of 10,000 psi, however such equipment is larger, heavier and more expensive and UMR evolved some design changes in the operation of the system that allowed a smaller, lighter system to be used at the lower operating pressure). Water reservoir Abrasive tanks

High pressure pump and motor Abrasive mixing unit Figure 1. Platform mounted pump and support equipment A system was built upon a simple platform (Figure 1) that could be lifted into the back of a pickup truck, for portable operation. The platform also contained a small reservoir for the gasoline to operate the system, a 110-gallon water reservoir and two pressure tanks that would hold abrasive for use in the cutting operation. The pump is manually operated, with the pressure in the lance controlled by the operator. Flow is diverted from

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the lance back into the tank to reduce the flow, and thus driving pressure, of the fluid going to the lance to the level that is most appropriate for the step being carried out, and for the operating field conditions. Because the pump is powered by a gasoline motor on the platform, and with the water reservoir, it becomes self-contained and is used as the supply unit for all three stages of the operation, even though in this particular field trial the full capacity was only required for the cutting operation. Fluid power from the pump to the various tools required for the different stages was supplied by a high-pressure hose connected to the metal tubing that is shown at the top right-hand corner of Figure 1.

1.2 Water Lance Prototype I A special control lance and triggering mechanism was built to control the high-pressure fluid pulses from the pump. To reduce wear in the system, and to minimize water use, when the water was not directed into the lance and out of the nozzle it was directed through a return hose to the main water storage reservoir on the platform shown in Figure 1. The lance’s main components consist of a power-washer lance with a spring-loaded triggering mechanism derived from the internal workings of a manually operated Arrow staple gun. The spring-loaded mechanism is used to release a pulse directly onto the wand’s original trigger. This is done mainly to preserve the functionality of the original wand so that the operator had the option to release a constant pressure flow for any desired time interval, as well as a controlled time pulse. The triggering required that the operator use both hands, a safety feature of the tool (Figure 2).

Figure 2: Lance Operation Figure 3: Mechanical triggering mechanism

A moving hammer and an angled plate act as the contact point between the two triggers. The hammer is attached to the spring-loaded carriage, which generates the mechanical pulse. The original trigger was re-fabricated out of aluminum mainly to reduce wear and to reduce the coefficient of friction between the hammer and it’s contact point as shown in figure 3.

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The two triggers were positioned linearly on the barrel (Figure 3). The first trigger is the original trigger supplied with the lance, and this is used to allow a pressure flow over a given length of time. The second trigger is for the spring-loaded system. With one pull of the trigger, a pulse is generated to the original trigger allowing the water valve to open for a controlled fraction of a second. The system will not reset until the spring-loaded trigger is released and allowed to return to the initial starting position. This feature allows the user to pull the spring-loaded trigger with a variety of force and speed but still to generate the same controlled volume water pulse from the nozzle. Tests were carried out to test the performance of the lance. Modifications were also applied to increase the durability and performance of the lance. In figures 4 and 5, the depth of penetration and the volume of the slug of water are shown as a function of the jet pressure. There is relationship between the volume of the water slug and the depth of penetration. The lance is an assembly of several components, which under the pressure will provide the optimum flow conditions. This in turn will control the volume of the discharged water slug and the depth of penetration. The increase in waterjet nozzle diameter shifts the curves toward a higher volume of water for the slug and gives a greater depth of penetration. A satisfactory depth of penetration was obtained when testing the jet in soil, and in loose sand. Were the device to be used in a harder and more compact material, such as a baked clay, then the parameters would need to be adjusted accordingly.

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.043 Nozzle

.030 Nozzle

.053 Nozzle

Figure 4: Penetration in sand as a function of jet pressure.

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11.11.21.31.41.51.61.71.81.9

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Figure 5: Volume of water used in a test as a function of jet pressure

1.2.1 Water Lance Second Prototype

The mechanical nature of the original triggering mechanism, while satisfactory for generating a defined short pulse, was not easily adaptable for adjusting the length of the pulse. This was of some concern since, in the early part of the triggering event the extraneous sound level that this generated was very high. Attempts to reduce the sound by wrapping the system with foam and noise damping material did not substantially decrease the sound to the required level. In this short time interval the spring loaded trigger mechanism generated noise, which was overlapping the acoustic signal generated by the slug of water interacting with a buried object.

A modified design was therefore developed, using an electrical trigger, whose duration could be controlled. This involved fitting a solenoid valve in-line between the initial trigger and the nozzle on the lance. The solenoid valve is a three way, normally closed valve. The nozzle end of the lance is fitted into the output port, which is normally closed. When the solenoid has not been activated the flow of water is passed through the open output port back into the water tank.

Initially it was planned to use a solenoid valve actuated using a 12-volt DC supply but delivery time on the desired valve was beyond the time available for the project. With a limited number of valve manufacturers, the design was modified to use a 110-volt AC valve. A solid-state relay is used in-line with a 9-volt DC battery to eliminate any electrical risk to the operator. A control box is used to control the length of the pulse

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and to ensure that only one pulse is released per push of the button. Until the button is released the solenoid valve will not reset and fire another slug. During the period of the shot the electronic trigger can be pushed again to stop the slug. The timer control box is powered by a 110-volt AC supply. A schematic of the modified lance can be seen in figure 6, and the overall view of the unit in Figure 7. Closer views of the nozzle assembly and the triggering unit are shown in Figures 8 and 9.

Figure 6: Schematic of the modified lance design

Figure 7. Lance assembled and in use.

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Lance to nozzle solenoid valve return line to pump Figure 8. Lance detail showing the solenoid valve and the return line to the pump. microphone

radar unit plastic shield jet nozzle jet of water Figure 9. Lance detail showing the nozzle, location of the microphone, radar unit and plastic shield

The bracket shown in Figure 7 was mounted to hold the button and handle assembly and uniformly balance the weight across the lance.

Because the radar unit was not used in the data analysis of results from the field trial it is appropriate to comment briefly on its use at this point, and also to explain why it was excluded.

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When the early work was carried out in the laboratory to determine the best methods for analyzing the signals generated from waterjet impact with buried objects, it was noted that the soil around the jet entry point would move in a way that was influenced by the buried object. To determine if this could translate into a measurable event, a Doppler radar unit was obtained and mounted adjacent to the nozzle in the test tank. The resulting signal showed (Figure 10) that the device was sensitive enough to pick up the velocity of the water as it entered the soil, (the slope to the curve) as well as to monitor the velocity of overall soil movement.

Figure 10 Radar signal from lab test of the Doppler radar unit, during a test. The angled line to the left of the picture shows the water slug traveling into the soil.

When the unit was taken to the field test, the behavior of the soil over the different objects was both more and less than anticipated. In a number of cases, however, since the ground had seen no rain since the holes were excavated for the mine, the overall ground movement was larger than anticipated, and may have been due to the water reaching the dimensions of the excavation hole. There was no ability built into the unit to discriminate this sort of movement from that of the ground over a mine. It was, however, remarkable that over the three days that the field tests were carried out most of the observers could tell when the jet hit a buried object, rather than just penetrating the soil. This is a feature of the tool that should be further developed, but which, due to time constraints has not been in, in this program. Similarly data from the use of the radar unit was not included in the analysis of the data from the field trial. It was noted that the maximum ground heave took a longer time to develop with the sand targets than it did with the soil.

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Figure 11. Detail showing a) soil before a shot b) soil during a shot at maximum displacement, c) both pictures superimposed with the displaced area highlighted in red.

The waterjet was operated at a pressure of around 2,200 psi for these tests in the field, and the nozzle was sized to a diameter of 0.05 inches. Thus the flow from the nozzle was around 3 gallons/minute were it to be run continuously. The nozzle exposure time is one second so that the amount of water used per pulse is around 10 cubic inches. As a measure of scale the plastic shield is 7.5 inches on each side. (The cast shadow from this plate is about the same size as the displaced area in Figure 12).

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Figure 12. Detail showing a) sand before a shot b) sand during a shot at maximum displacement, c) both pictures superimposed with the displaced area highlighted in red.

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2 Detection Approach The sound of a high pressure waterjet cutting through the soil cover and striking an object may be used to detect and even identify that object. The sound is a function of the waterjet, the physical characteristics of the object, such as its shape, elasticity, and mass, and the characteristics of the surrounding environment. This research in waterjet based landmine detection is based on the premise that the acoustic signal produced by the impingent waterjet is characteristically different for different types or classes of objects. Our objective is also to identify the object in the path of the waterjet, when one is present. For the purpose of the current research, waterjet based mine detection is being developed as a confirmatory sensor. Thus it is assumed that an alternative area-detector such as a metal detector has been employed to identify the suspected location of the mine. Due to high sensitivity, often a metal detector may provide a false positive signal for small metal debris. In such a case an absence of any physically significant object indicated by waterjet will indicate that the detection by this alternative sensor was actually a false alarm. Where a physically significant object is present, analysis of the acoustic signal may be able to classify the object as either a known mine or as a harmless object. Three methods of detecting and classifying a buried object using the sound of a waterjet impact were investigated. The methods were based on a) using a Weighted Density Distribution (WDD) to identify unique features in the recorded sound over time, b) using a Hidden Markov Model (HMM) and Cepstral coefficients to model the system as a random first order Markov process, and c) using a maximum likelihood estimator applied to the power spectral density of the recorded signal. A variety of methods to improve the accuracy of each of these techniques were explored. The theory and rationale behind each of these three methods is summarized in detail in the following three sub-sections.

2.1 Basis Functions Applied to Temporal Acoustic Data In this research, the acquired acoustic signals measured by the microphone for the different water jet squirt encounters were processed to compute temporal features based on the application of basis functions. These features were applied for object/no object, landmine/no landmine and harmless object/landmine discrimination. Weighted density distribution (WDD) functions have been applied to temporal metal detector signals in hand-held units for computing spatially distributed features used for landmine/no landmine discrimination 18, 19, 20 . The application of the WDD functions to acquired acoustic signals measured by the microphone for the water jet encounters is intended to quantify three components of the temporal acoustic signal:

1) low frequency content of the acoustic signal, 2) acoustic signal transition information and 3) symmetry of acoustic signal response.

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Figure 13 below shows the WDD functions that are correlated with windowed acoustic signals for computing 12 global features for discrimination purposes. In the initial set up at the UMR Rock Mechanics sand box facility, a significant number of water jet squirt encounters were performed on landmine simulants, and harmless objects such as rocks and sand only, these are referred to as clutter encounters. Preprocessing operations such as applying the Teager operator (used in speech modeling) 14 and median filtering were examined. Acoustic signal discrimination was performed on an object/no object basis. The primary discrimination technique explored was based on K-means clustering and nearest neighbor algorithms 15. WDD features computed from clutter acoustic signals were used to generate a model representation for clutter using K-means clustering. Signals with computed WDD features exceeding a specified distance metric from the nearest cluster were labeled as objects. Otherwise, the signal was labeled as no object.

Figure 13: WDD functions [1] correlated with windowed, preprocessed signal.

WDD Functions

For subsequent acquired acoustic signal data, combinations of frequency histogram features and WDD features were applied to discrimination among clutter, landmines and harmless objects. The frequency histogram features were computed based on dividing the frequency domain into 500Hz frequency bins. The frequency content of landmine and harmless object acoustic signals were mapped and summed into frequency bins. Plots of the frequency histograms were examined to identify potential frequency ranges (characterized as bins) which provide discrimination information between landmines and harmless objects. These summed frequency content in the discriminatory histogram bins were used as features. A two-tiered model-based approach was used to discriminate among the three classes (clutter, landmines and harmless objects). The first tier was the previously described clustering-based model to represent clutter. Acoustic signal feature vectors labeled as objects were passed to the second tier, where a clustering-based model was developed to represent landmines. K-means clustering was performed on the frequency histogram features. The nearest neighbor algorithm to the landmine clusters

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was applied for landmine/no landmine discrimination. For the blind test of the algorithms at the field test site, landmine/harmless object was performed. Evaluating the features from the calibration data acquired at this site, it was determined that the WDD features provided better discrimination capability than the frequency histogram features. Consequently, the discrimination algorithm used for the blind test water jet encounters utilized the clustering and nearest neighbor algorithms to provide a model-based representation for landmines. A more detailed presentation is given in the following sections for acoustic signal analysis and discrimination using WDD basis functions.

2.2 Hidden Markov Model Approach When the water jet cuts through the soil without any obstacles/object in its path, a sound is produced due to the water exiting from the nozzle and waterjet-soil interaction. This signal is generally dominant in the higher frequency spectrum. However, if there is an object in the path of the waterjet, a different sound is produced which in general has higher low frequency power. Moreover we expect this sound to be characteristics of the object encountered. Thus if a physically significant object is present, we may be able to classify the objects as mine or a harmless object or even identify the objects (such as rock, wood, type of mine etc) using the characteristically different sound produced by the objects of different class. In this section we describe a detection approach based on Hidden Markov Model (HMM) of the dynamics of the waterjet-soil-object interaction. The observation feature vector for discrimination is based on Linear Prediction Coefficient and Cepstral analysis which captures the local time-variant spectral characteristics of the waterjet-soil-object interaction. Details of the detection algorithm are discussed in this section. Results for the field tests are summarized in sections 4.2.

2.2.1 Motivation for using Hidden Markov Models For automatic target detection and discrimination we have explored Hidden-Markov Models (HMM) in order to model the dynamics of the waterjet-soil-object interaction. Figure 14 shows a simple illustration of the waterjet set-up and expected waterjet-soil-object interaction. Different physical states of the waterjet as it cut through the sand and hit the buried object can be described as follows.

Stage – 1: Water jet exiting the nozzle and passing through the air. Stage – 2: Water jet impacting the soil. Stage – 3: Water jet cutting through the soil. Stage – 4: Water jet impacting an object (if present). Stage – 5: Water jet loses its momentum and pressure decays at the bottom of the

hole (if no object encountered). Since the speed of the waterjet through air is high and the distance of the nozzle to the soil surface is only a few inches, we are unlikely to “see” stages 1 and 2. Thus we can describe any acoustic signal as a combination of three states corresponding to (1) interaction of a jet with soil, (2) interaction of a jet with the object (when present) (3) decay of the jet. The presence of the object is dictated by the presence or absence of state

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2. Moreover different characteristic signals associated with this state may be used to identify the object. The model is called Markov because the probability of the presence of the subsequent state is dependent on the current state of the model (first-order Markov). Also note that the identity of these physical states is not known to the user and only the sounds produced as a result of the pulse of the waterjet are observed/heard by the operator. Since the physical states themselves are hidden from the observer this analysis is called using “Hidden” Markov Models. In general we would expect different Markov models for different types of buried objects (due to different characteristics of state 2). The HMM for a given object is described in terms of the probabilities of state transition and the probability of a state corresponding to a given observation signal 21, 22 .These probabilities and hence the HMM’s can be learned using signals of known identity from the calibration lanes. Once the HMM has been learned for a given class or identity of object (for example a known mine), then the detection problem is reduced to finding a conditional probability:

),class( classnOp Λ Here is the observation sequence for the nth pulse and nO classΛ is the HMM for the class. For a multi-classification the above conditional probability can be obtained for each class of object and the class with highest conditional probability defines the identity of the buried object.

Figure 14: Schematic of the waterjet-soil-object interaction

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2.2.2 Feature Extraction As discussed earlier, the observation signal for waterjet based mine detection is the sound produced by the waterjet-soil-object interaction during the firing of the waterjet pulse. This raw acoustic signal is reduced to an observation sequence consisting of multi-dimensional feature vectors that capture the evolution of the waterjet-soil-object interaction. For the current research we have adopted Cepstral analysis to define the feature vector for the waterjet signal. Similar features are often used in speech processing for speech recognition and analysis. This is a block-processing model in which a feature vector is computed for an overlapping frame of N samples (of the acoustic signal) to define an observation sequence O. The observation sequence On for the nth pulse is obtained as follows 21:

1. Blocking raw data into frames: Sections of N consecutive samples (we use N = 4800 corresponding to 108 ms of the signal) are used as a single frame. Consecutive frames are spaced M samples apart (we use M=1600 corresponding to a 36 ms frame spacing providing 72 ms of frame overlap).

2. Frame windowing: Each frame is multiplied by a N-sample window (we use a

Hamming window) w(n) so as to minimize the adverse effects of chopping an N-sample section out of a running acoustic signal.

3. LPC/Cepstral analysis: For each frame, a vector of Pth order LPC coefficients

are computed (we use P=8). An LPC derived Cepstral vector ( ) is then computed up to the Qth component, where Q = 1.5*P.(1)

)(mCt

4. Cepstral weighting: The Q-coefficient Cepstral vector Ct(m) at time frame t is

weighted by a window Wc(m) of the form,

QmQmQmWc ≤≤+= 1 );/sin(2

1)( π

5. Delta Cepstrum: The time derivative of the sequence of weighted Cepstral

vectors is approximated by a first-order orthogonal polynomial over a finite length window of 2K+1 frames (we use K=2). The Cepstral derivative is computed as,

QmmGmCK

Kkt ≤≤=Δ ∑

−=1 )(KC)( k-t

where, G is the gain term chosen to make the variances of and is made equal (G = 0.375) and is the weighted Cepstral vector.

)(mCt )(mCtΔ)(mCt

6. Feature vector: The feature vector for each frame (t) is then a concatenation of

the weighted Cepstral vector and the corresponding delta Cepstral vector. { })(C)(C tt mmrt Δ=

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7. Raw observation sequence: The feature vectors for each frame t are concatenated to form the raw observation sequence for each pulse:

{ }Tn rrrR L21= where T is the total number of overlapping frames for the whole duration of the pulse.

Note that the Cepstral coefficients characterize the logarithm of the amplitude spectrum of the observed signal and are thus better suited for our detection problem when compared to the linear predictive coefficients themselves. The waterjet could be thought of as a source signal (impact). The recorded sound at the microphone can be thought of as the response of the buried object to this waterjet (impact) signal. The characteristic signature of this object could then be modeled in terms of its impulse response b(t). Assuming that the source signal of the waterjet is s(t) the recorded signal x(t) is given by

)()()()(or )()(*)()( ffSfBfXttstbtx Ν+=+= η where )(tη is the additive noise component which may be due to the background noise (such as that from the high-pressure pump) or made by the waterjet exiting the nozzle. For the purposes of the current discussion we will assume that this component can either be neglected or has been filtered before hand. Note that the spectral characteristics of the source signal s(t) are not fixed and may vary due to factors such as change in waterjet pressure and variation in the standoff distance from the nozzle to the surface, or in the buried distance to the object. The quantity of interest here is the signature of the object modeled by b(t) while the source signal s(t) could be considered as undesirable noise which could obscure this signature. The logarithm of the amplitude spectrum of the observed signal is given by

)(log)(log)(log fSfBfX +≈ Thus while variation in the spectrum of the source signal will affect the spectrum of the observed signal in a multiplicative manner, the corresponding effect on the logarithm of the spectrum is additive. As a result the Cepstral coefficients are more robust to any variation in the source signal. Figures 15 and 16 show the plot of a sequence of feature vectors for waterjet induced signals corresponding to background noise and impact with the mine respectively. Each subplot in these figures shows the feature vector { })(C)(C tt mmrt Δ= starting in the lower left corner and scanning up and to the right. Also the set of all feature vectors for a given pulse define the raw observation sequence { }Trrr L21nR = .

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Figure 15. Plot of a sequence of feature vectors for the signal produced by the background (wav file taken form calibration lane soil 2” dataset).

Figure 16. Plot of a sequence of feature vectors for the signal produced by a PMN mine (wav file taken form calibration lane soil 2” dataset).

Comparing figure 15 and 16 we can clearly see the differences between the Cepstral feature vectors of the waterjet induced signal for the background and those for the mine. It is this distinction among the feature vectors that we seek to exploit for mine detection and discrimination. Also note that the feature vectors are very similar for approximately

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the first 4 frames which show that the starting portion of the pulse for separate firings over different objects share similar characteristics.

2.2.3 Estimation of the Hidden Markov Model An HMM is characterized by three sets of probability matrices: the transition probability matrix, the observation probability matrix, and a prior probability matrix. For the current analysis we have assumed that the system always starts in state ‘one’ so that the prior probability matrix is fixed. Given the current state, the transition probability matrix gives the probability of occurrence of the new state. Also for a given state, the observation probability matrix assigns a probability to the occurrence of the new observation feature vector. In order to avoid the computational complexity associated with continuous observation probability density function, the feature vectors in the observation sequence are often quantized into a set of finite symbols using vector quantization. The symbols are assigned according to a minimum distance to the prototype vectors stored in the codebook 23 . The codebook can be estimated using the available calibration data. Given the raw observation sequence { }Tn rrrR L21= the discrete observation sequence is defined as . { }Ton ooO L21= Here

{ } VorVQo iii ∈= ;

where V is the set of all possible observation symbols. In the following we briefly discuss the estimation of the Hidden Markov Model given a set of calibration data. A more detailed discussion can be found in an excellent tutorial by Rabiner 21 1. At any given time the system could be in one of the N states. Although the states are

hidden, in many practical applications there is some physical significance attached to the states of the model. We use an ergodic model, i.e. any state can be reached from any other state. The states of the system at time t is identified as

. { }Nt SSSq L21∈

2. The feature vector at any time t is quantized to M symbols using a pre-defined codebook. The resulting observation symbol at any time t are identified as

{ }Mt VVVo L21∈ .

3. Given the current state of the system at time t as it Sq = , the probability of state at time t+1 is given by the transition matrixjt Sq =+1 { }

NxNijaA = , where,

[ ] NjiSqSqPa itjtij ≤≤=== + ,1 ; |1 ,

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4. Given the current state of the system at time t as it Sq = , the probability of occurrence of observation symbol k at time t ( kt Vo = ) is given by the observation matrix where, { }NxMikbB =

[ ]Mk1Ni1

;|≤≤≤≤

=== itktik SqVoPb

5. The probability of the system in state i at time t=1 ( iSq =1 ) is given by an initial

probability matrix { } xNi 1π=Π where, [ ] NiSqP ii ≤≤== 1 1π

Thus the HMM for the system with N states and M observation symbols is parameterized in terms of three probability matrices A, B, and Π . We use the notation,

to indicate the complete parameter set of the model. Given a set of observation sequences for the system, the HMM parameter

{ xNNxMNxN BA 1,, Π=Λ }{ }xNNxMNxN BA 1,, Π=Λ

can be estimated using the Baum-Walsh method 22 . Given the HMM for class l, { }xNNxMNxNl BA 1,, Π=Λ

}To, the probability that the observation

sequence was a result of a first order Markov process defined by is given by the conditional probability of class l given

{n ooO L21=

lΛ lΛ and nO

( ) ( ) ( )∏=

−=

ΛΛ=ΛT

kqqoqq

nlnnln

kkkkab

QPQOPOlP

111

.|ˆ,ˆ|,|

π

where is the optimal state sequencenQ { }Tn qqqQ L11 = that maximizes the above conditional probability. Thus

( )[ ] { }TnlnQn qqqQOlPQn

L11 ;,|maxargˆ =Λ=

For the waterjet based detection purposes a HMM is estimated for each class of object to be detected. Once the HMM has been learned for a given class or identity of object (for example a given mine), the object is said to belong to class l if the conditional probability ( lnOp Λ,l ) is above some threshold. For a multi-classification the above conditional

probability can be obtained for each class of object and the class with highest conditional probability defines the identity of the buried object. Thus

( )[ ] { }tionclassifica ;,|maxarg ∈Λ= lOlPL lnl

22

2.3 Maximum Likelihood Applied to Power Spectral Density When using a high-pressure waterjet to detect buried landmines, an acoustic signal measured by a microphone, r(t), may result when the waterjet strikes an object or strikes only soil (a “miss”).

Figure 17. Basic geometry of the test case Objects may be classified as either harmful or harmless or using a more detailed specification (e.g. as rock, wood, PMA mine, etc). Because we were asked to distinguish between several different objects of similar type in our field tests, we developed an approach based on maximum likelihood theory to use r(t) to decide between several different hypotheses. Hypotheses might include:

H0: “no object is present (miss)” r(t) = s0(t) T1 ≤ t ≤ T2 H1: “object is a rock” r(t) = s1(t) T1 ≤ t ≤ T2 : : : : Hn: “object is a root” r(t) = sn(t) T1 ≤ t ≤ T2

The number and type of hypotheses depend on the number and type of objects and conditions that might be encountered within a particular test. If an object is detected, the harmless/harmful decision can be made based on the type of object identified (e.g. Hharmless = {H1, H2, H3}, Hharmful = {H4, H5}). It is important to be able to distinguish a miss from a hit a) so that if multiple signals over a particular object are used to identify an object, misses can be removed from the decision and b) because a human operator can construct a mental picture of the object’s size and shape simply by firing the jet at the object several times and remembering where an object was or was not struck. In our field tests, we found the operator could quickly form a good idea about the nature of the object based on his own observations of the waterjet. Previous research has shown that the sampled microphone data, r[n], becomes quasi-stationary approximately 250 ms after the waterjet is turned on over dry sand 9. Within

23

the quasi-stationary period, r(t) can be modeled well as a gaussian stationary random process 10. As such, r(t) can be characterized by its power spectrum, Sr(f). The power spectrum derived from any particular signal will depend on a set of physical parameters, Ө, such as object type, depth, soil conditions, and so forth. In discrete form, the probability density function for a particular parameter set Өi is given by

)()(2/1

2/2/1

1

)2(1),( ii

Ti xxCxx

ni

i eC

xf −−− −

=π

θ

where,

,

][

][][

1

0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

nr

r

r

fS

fSfS

xM

is a vector of measured power spectral density values at discrete frequencies f0 through fn, n is the number of discrete frequencies available, and Error! Bookmark not defined. ix and Ci are the vector mean and cross correlation matrix, respectively, of the power spectral density of the random process generated by physical parameter set Өi. For simple hypotheses, as stated above, a widely accepted solution for the best choice among the set of hypotheses {Hj} is given by the hypothesis, Hi, for which 11:

jxfxf ji ∀≥ ),(),( θθ where the search space {Өj} is defined over all possible physical parameters that may be encountered in a particular test. The hypothesis Hi is a “maximum likelihood” solution. As the parameters ix and Ci are unknown, they must be estimated from available calibration data. For a set of measurements, {rj(t)}, taken under similar known conditions, ix and Ci can be estimated as

∑=j

ji xm

x 1ˆ

and

∑ −−−

=j

Tijiji xxxx

mC )ˆ)(ˆ(

11ˆ

where m is the number of measurements and

24

.

][

][][

1

0

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

nr

r

r

j

fS

fSfS

x

j

j

j

M

Best estimates of are obtained when m>>n. When m ≤ n, is singular. iC iC Proper application of this maximum likelihood approach requires a large number of measurements for each possible Өi. Unfortunately, a large number of calibration measurements are not always available. In this case, principle component analysis can be used to improve results or make the problem tractable when m ≤ n 11. The matrix can be decomposed into its eigenvalue and eigenvector components as 12

iC

T

i UUC Λ=ˆ

where U is a matrix of eigenvectors ui, [ ],||| 10 nuuuU L= ),( 0 ndiag λλ K=Λ ,

and iλ are the eigenvalues of given in non-increasing order. The principle components

of are given by the eigenvalues iC

iC jλλ K,0 for which ελ >j , where є is a constant chosen heuristically. The probability density function can be re-written to perform calculations for the principle components of Sr[f] as

)()(2/1

2/2/1'

'1''

)2(

1),( iTT

i xxUUxx

ji

i exf −Λ−− −

Λ=

πθ

where and ),( 0

'jdiag λλ K=Λ [ ]juuuU ||| 10

' L= . The number of principle components may vary between parameter sets for a given constant є. In general, for a given x, the size of ),( ixf θ increases exponentially with an increasing number of components j. Since the components are orthogonal, this increase can be seen by the decomposition of ),( ixf θ as the joint probability of individual components jλ ∏=

jii xfxf

j),(),( θθ λ

where

)()(2/1

2/12/1

1

)2(1),( i

Tjjj

Ti

j

xxuuxx

ji exf −−− −

= λλ πλ

θ .

25

To compare accurately between parameter sets with a different number of principle components, j, the jth root of the probability density function was taken before comparison. Experiments were performed where the same number of principle components was used for each parameter set rather than adjusting the result using the jth root, but results were much poorer in this case. A single object is rarely interrogated by the waterjet only once. The deminer will typically fire the waterjet at a suspect object several times before making a decision about its type. Accordingly, the maximum likelihood decision process should take multiple shots at a single object into account. As the recorded signals over multiple shots may reasonably be assumed to be independent, the probability density function for a set of recorded sounds over a single object may be given by ),(),(),(),,,,( 2121 ikiiik xfxfxfxxxf θθθθ LL = . For the set of shots {xk}, the maximum likelihood solution is given by the hypothesis, Hi, for which:

jxxxfxxxf jkik ∀≥ ),,,,(),,,,( 2121 θθ LL In the process of interrogating an object with the waterjet, the deminer is likely to miss the buried object several times. These misses should be removed from the decision step given above. To account for these misses, the decision process was split into two steps. In the first step, the value of the probability density function was determined for individual shots. Shots that were most likely to be misses (i.e. were background or soil shots) were removed. Experiments have shown that misses can be detected and identified as such, relatively accurately. The remaining shots were assumed to be “hits” and were used to form the value of the joint probability density function over the group of shots. The values of the joint probability density functions among physical parameters Өi were then used to classify the buried object type.

26

3 Detection in the Laboratory The water jet acoustic analysis research for detecting landmines was conducted on two data sets acquired at the University of Missouri-Rolla (UMR) Rock Mechanics sand box facility, one data set at the UMR Rock Mechanics outdoor clay and sand lanes and calibration and blind test data over soil and sand lanes at a government provided set of test lanes. The following presents data and analysis developed for the two datasets acquired in the Rock Mechanics laboratory.

3.1 Data and Data collection

3.1.1 UMR Sand Data Collected in 1999 The initial data set was collected at the UMR Rock Mechanics sand box facility in 1999. The experimental setup used two microphones and two water jets as shown in the schematic in Figure 18. For this experimental data collection, water jet squirts were performed over an arrayed region within the sand box experimental location. The water jet unit movement was computer controlled over the grid. This also had the effect of limiting vibrations in the water jet unit. Two types of water jet squirt schemes were used for data collection, including alternate and simultaneous squirting of the two water jets. Figures 19 and 20 show the data recording schemes for the alternate and simultaneous squirts, respectively. The following criteria were used for data collection:

• Microphones are kept on throughout the trial. • In alternative firing, when a water jet is at halt the microphone above it records

the data due to the firing of the other water jet.

27

1 inch

2 inch

Sand

Microphone

Water jet 4 inch

Figure 18: Experimental setup for data collection at UMR sand box in 1999.

Figure 19: Example of simultaneous pulse encounter.

28

Figure 20: Example of an alternate pulse encounter. The following experimental parameters were used for the sand box and water jet setups:

• Pressure: 3,000 psi • Depth of the mine placed: 1.5inches below surface of sand • Nature of Soil: Fresh sand • Sampling Rate: 44,100 samples per second • Microphone Type: Peavey

• Cardiod Unidirectional polar response • Frequency Response: 50-14,000 Hz •

For this data collection, a total of 208 pulses were performed, including simultaneous and alternative pulse encounters. Within the grid box, pulses were fired at harmless objects (rocks), simulated mine types and sand. Table 1 below shows the number of pulses fired for each class, the total number of samples of acoustic data collected from the microphone and the type (alternate or simultaneous pulse). The final column of Table 1 shows 1 or 2 pulses indicating alternate (2) or simultaneous (1) pulse cases. A total of 163 pulses were fired over clutter or sand only, 19 pulses were fired over landmines and 10 pulses over rocks. It should be noted that the sand in the laboratory sand box became stiffer over time due to continuous squirting of water into the sand box.

29

Exp. 1

Trial set

# Of trials

Clutter Mine Rock Total # sample

Squirt

1 38 36 2 0 44100 1

2 16 16 2 0 61740 2

3 18 16 2 0 61740 2

4 18 16 2 0 61740 2

Exp.2 1 10 5 5 0 39690 1

2 4 2 0 2 39690 1

3 14 7 3 0 83790 2

4 10 5 0 4 83790 2

Total 20 128 103 16 6

Total Signals

163 19 10

Table 1: Summary of data collected from sand box.

For the data collected three experimental approaches were examined for signal classifications as object/no object.

3.1.2 UMR Sand Data Collected in July 2002 The second set of data was collected at the UMR Rock Mechanics sand box facility in July of 2002. There were two differences in the water jet setup for this data from the initial data collection setup. First, a more sensitive directional microphone was used. The microphone was a Schoeps CCM41 supercardioid microphone1, which has a highly linear response over the 40 Hz -20 kHz frequency range. Second, the microphone setup around the water jet unit was modified to have one microphone mounted similarly to the setup in Figure 18 (tank microphone) and one floor microphone mounted at the side of the sand box pointing directly toward the water jet. Only data from the tank microphone was used for acoustic data collection for the trials or encounters in the sand box. Again, the water jet unit movement was computer controlled over the grid. This also had the effect of limiting vibrations in the water jet unit. 1 http://www.schoeps.de/E/mk-ccm41.html

30

The experimental parameters used for data collection include: • Nozzle diameter: 0.043 inches • Object depth: approximately 2.5 and 3.0 inches • Minimum water jet pressure: 4800 psi • Maximum water jet pressure: 5000 psi • Sand box array or grid size: 5 shots x 5 shots • Spacing between objects or encounters: 4.5-5.0 inches • Tank microphone: 24 bit DAC • Floor microphone: 24 bit DAC • Video radar (large): 24 bit DAC • Pressure: 16 bit DAC • Sampling rate: 96 kHz

All microphones were run through ALesis Studio 12R mixing board. As with the initial data collection, simulated landmines were used this included a PVC cap. The harmless objects used in this data collection include a wood block, irregular rock and screw. Figure 21 (a) presents the array or grid configuration as seen from the water jet squirts. Figure 21 (b) shows the water jet setup with microphone position and Doppler radar position (to the right). Figure 22 shows the uncovering of the PVC cap mine from a water jet trial.

(a) (b)

Figure 21: Water jet and sand box setups for data acquisition. (a) Illustration of water jet array or grid used in the UMR Rock Mechanics sand box facility. (b) Water jet, microphone and Doppler radar setups.

31

Figure 22: Illustration of PVC cap mine uncovered from water jet pulse. For this data collection, there were 129 clutter pulses (pulses over only sand), five pulses over simulated landmines and 13 pulses over harmless objects. For the data collected three experimental approaches were examined, using as signal classification the object/no object criterion.

3.2 Application and Results Using WDD Approach

3.2.1 UMR Sand Data Collected in 1999 Due to the significant disparity in the number of clutter squirt cases over landmine and rock cases, a clutter model-based approach was investigated. Several features were explored to characterize and distinguish clutter from landmines and rocks. These initial features included computing the Fourier Transform of the windowed signal, computing the power spectrum density in the acquired acoustic signal, and computing weighted density distribution (WDD) function features 13 over the acquired acoustic signal.

Signal filtering approaches were also examined, including normalizing the acoustic signal using the Teager operator (commonly used in speech modeling) 14 and normalizing the acoustic signal using median filtering. Signal windowing in the simultaneous squirt case involved omitting the first and final thousand samples of the acoustic signal. Signal windowing in the alternate squirt case involved determining the midpoint of the acoustic signal, partitioning the data into two squirts. The first and final thousand samples were windowed out of the two signals for feature extraction and classification purposes. The six WDD functions are point-to-point correlated with the windowed, processed signal to generate six features. The absolute difference signal sequence is determined and point-to-point correlated with the windowed, processed signal to generate six additional features. The twelve resulting features were used for signal discrimination. The clutter data was divided into training and testing sets for classifier development. From the training data, the mean and standard deviation for each feature was computed. Then, the training data was normalized by subtracting the mean and dividing the standard deviation. The corresponding testing clutter data and the landmine and rock data was

32

also normalized by subtracting the corresponding training feature mean and dividing by the training feature standard deviation. Based on preliminary results, preprocessing using the Teager operator and median filtering (sample and its immediate neighbors) were performed with the weighted density distribution function features computed from the preprocessed signals. The classifier explored included training a K-means algorithm 15 using clutter for generating cluster centers to represent clutter. A nearest neighbor algorithm with a threshold distance to the nearest cluster was used for classifying the signal as object or no object was investigated. Encounters with a distance to the nearest cluster of greater than the threshold were classified as objects. Otherwise, the encounter was classified as clutter (no object). In this research, the 208 clutter encounters were randomly separated as 80% training data (166 clutter encounters) and 20% testing data (42 clutter encounters). For threshold determination, a receiver operating characteristic (ROC) curve was used, whereby the threshold for classification was varied from the maximum training distance to the nearest cluster to zero. Training and testing classification rates were computed for each threshold. Table 2 shows an excerpt from the ROC curve for WDD features computed from the Teager operator preprocessed signal. Table 3 presents an excerpt from the ROC curve for WDD features determined from the median filtered preprocessed signal. Tables 2 and 3 show the percentages correctly classified clutter for the training data, testing data, landmines correctly labeled as objects and rocks correctly called objects. From the experimental results, the WDD features appear to provide object/no object discrimination capability. Specifically, the WDD features seem to highlight acoustic signal transition information that is distinctive for objects and no objects. Figure 3 shows an example for an alternate squirt case where the WDD features correctly distinguish the first (or left) encounter as an object from the second (or right) encounter as clutter. The WDD features highlight the symmetry of the acoustic signal response for object/no object discrimination. The results from Tables 2 and 3 also appear to indicate that median filtering improves the object/no object classification results over the Teager operator. Teager Operator Preprocessing Training

Data Testing Data

Threshold T % Correct Clutter

% Correct Clutter

% Correct Landmine Labeled as Object

% Correct Rock Labeled as Object

1.76 85.4 81.8 63.2 60.0 1.16 80.8 72.7 68.4 60.0 0.96 73.8 63.6 84.2 60.0 0.81 71.5 60.6 84.2 70.0 0.66 63.8 54.5 84.2 90.0 Table 2: Selected points from ROC curve using Teager operator preprocessing for WDD features.

33

Median Filtering Training

Data Testing Data

Threshold T % Correct Clutter

% Correct Clutter

% Correct Landmine Labeled as Object

% Correct Rock Labeled as Object

12.65 98.5 93.9 73.7 80.0 8.06 96.9 90.9 73.7 100.0 7.56 96.2 90.9 78.9 100.0 4.51 89.2 72.7 84.2 100.0 3.41 83.8 69.7 89.5 100.0 Table 3: Selected points from ROC curve using median filtering for preprocessing for WDD features.

Mine Clutter

Figure 23: Example of WDD features emphasizing transition information in discriminating objects (landmine) from no object (clutter).

34

3.2.2 UMR Sand Data Collected in July 2002 Windowing of the acquired acoustic signal for each trial or encounter was based on processing approximately 1 second of acquired data. For each acquired signal, 90,000 samples were processed (samples 10,001-100,000). The first 10,000 samples of the acquired signal tended to include extraneous sounds such as the water jet pump. For this data set, a clutter model-based approach was investigated for object/no object discrimination. As previously performed, weighted density distribution function features were used to provide the basis for object/no object discrimination. For these experiments, code development was performed using Matlab®. Accordingly, the Fuzzy C-Means (FCM) algorithm 16 was used in place of the K-means algorithm examined for the previous data set. Based on comparing results from the previous data collection using the K-Means algorithm and FCM, there was little difference in classifier performance. In this research a two-tiered classifier was investigated. The same approach performed for the initial data set was used for object/no object discrimination. Specifically, a clutter model-based approach was examined where FCM clustering was performed on training clutter WDD feature vectors to obtain cluster centers to represent clutter. Based on applying the Davies-Bouldin statistical analysis technique 17 used in Partek® on the 100 clutter training feature vectors, eight clusters were chosen. The FCM algorithm in Matlab was used for obtaining the eight clusters to represent clutter. Then, for each training and testing WDD feature vector, the Euclidean distance was computed to each of the eight clusters, retaining the distance to the nearest cluster for classification purposes. As previously performed, each training and testing pattern was classified as clutter (no object) if the vector’s Euclidean distance to the nearest clutter cluster was less than or equal to a specified distance threshold. The second tier of the classification scheme was based on the frequency response distribution of landmines and objects. Specifically, separate cumulative histograms of the frequency response, as computed from the Fourier transform, were computed over the landmine data and the harmless object data. The histograms consisted of bins of 500 Hz increments. Cumulative, separate sums within each bin were maintained of the frequency response contribution at that bin for all landmine encounters and for all harmless object encounters. The cumulative histogram for the landmine encounters were normalized based on summing all bins and dividing each bin by the total sum. The harmless object cumulative histogram was similarly normalized. Plots of the normalized histograms were examined for responses in frequency ranges (bins) that differed between landmines and harmless objects. The sum from the individual signals within the specified histogram bins were used as features for discriminating landmines from harmless objects. Because of the limited number of squirts for landmines and harmless objects, the training and testing sets for landmines and harmless objects were identical. A similar scheme FCM, nearest neighbor scheme was investigated for differentiating between landmines and harmless objects. For trials and encounters based on the WDD

35

features labeled as objects, landmine/harmless object discrimination was performed. Based on the training data for landmines, the frequency ranges 3.5 kHz-6.0 kHz and 19.5 kHz-21.0 kHz or bins 7-11 and 39-41 were used as features (eight total features) for FCM clustering. Because of the limited number of landmines, only two clusters were used. As with the clutter representation, the Euclidean distance was computed from the histogram-based features to the nearest landmine cluster center. If the Euclidean distance is less than a specified threshold, the trial or encounter was labeled as a landmine. Otherwise, the encounter was called a harmless object. Preliminary analysis of applying this approach for developing a model representation for landmines and for harmless objects showed that modeling or representing landmines yielded better discrimination results. In order to increase the processing speed for feature calculations, we also examined the impact on classification results for median filtering versus no preprocessing operations. Based on experimental results, we found little impact of performing no preprocessing operations on the data prior to feature calculations. Table 4 below shows the experimental results for object/no object using no median filtering for preprocessing the signal data for WDD feature calculation. Table 5 below presents the experimental results for landmine/harmless object using no preprocessing for the frequency bin feature calculations. The landmine/harmless object classifier is the second tier of the cascade scheme presented. For an encounter to be classified as a landmine or a harmless object, the encounter must be called an object from the first tier (clutter model-based approach). For these experimental results, threshold selection for object/no object was based on obtaining 100% correct training clutter (no object) recognition from the clutter model-based approach. Threshold selection for landmine/harmless object was based on obtaining 100% correct training landmine discrimination for landmine model-based approach based on the frequency histogram bin features. In terms of training and testing sets, 20% of the clutter encounters were independent testing cases and all of the landmines and harmless objects were independent testing cases for object/no object discrimination. All of the landmine and harmless object data was used as training data for determining the frequency ranges (bins) that were used to distinguish landmines from harmless objects. Thus, for the second tier of the two tiered classifier (landmine/harmless discrimination based on the landmine model-based approach), there was no independent testing set. Because of the limited number of encounters for landmine and harmless objects in the data set, all of the landmine and harmless object data was used for training. Training

Data Testing Data

Threshold T for Object/No Object

% Correct Clutter

% Correct Clutter

% Correct Landmine Labeled as Object

% Correct Harmless Objects Labeled as Object

114.8 100.0 100.0 100.0 92.3 Table 4: Object/no object discrimination results from clutter model-based component (first tier) of two-tiered classification scheme.

36

Threshold T for Landmine/

Harmless Object

% Correct Landmine Labeled as Landmines

% Correct Harmless Objects

Labeled as Harmless Objects

8.81 100.0 76.9 Table 5: Landmine/Harmless Object discrimination results from landmine model-based component (second tier) of two-tiered classification scheme. All landmines and harmless objects were used in the training the landmine model-based component. These results indicate all landmines correctly classified as landmines and harmless objects classified as harmless objects, regardless if the encounter was misclassified from the first tier. Based on the experimental results from Table 4, the WDD features appear to provide discrimination capability for distinguishing objects from non-objects. These results are based on independent training and testing sets in generating the classification results. Despite the training and testing sets being identical for the landmine/harmless object discrimination component of the two-tiered classifier, the frequency bin features seem to have discrimination ability for distinguishing landmines from harmless objects.

3.3 Application and Results Using the HMM Approach Performance of the HMM based detection algorithm was evaluated on a data set similar to the one described in section 3.2.1. Encouraging results were obtained. Some of these results were reported as part of earlier reports and can be summarized by four consecutive illustrations of the power of the HMM approach to identify the presence of a mine from the raw data (Figures 24 – 27). The July 2002 data was not extensive enough to do any reasonable evaluation of the performance.

Figure 24. A 10 by 10 grid of raw acoustic data from sandbox pulses. The “red zone” identifies the position of the inert mine.

37

Figure 25. A Plot of the Low Frequency Spectra derived from the data in Figure 24. (Red zone is the mine)

Figure 26. One of the 14 feature vectors for all the 100 wav files (Red zone is the mine)

Figure 27. Optimal state sequences for HMM with 3 states. A transition to state 3 indicates the presence of a buried object.

38

4 Detection in the Field

4.1 Data and Data collection The final data set examined for this study was collected at a specially prepared set of mine test lanes at a government facility during August 2002. At this site lanes were made up in two soil types, the local clay/soil mixture and imported sand. The lanes were set up to allow calibration over known targets, at known depths and then to allow blind testing of the algorithms presented in this report to this point to identify unknown objects buried in each medium.

4.1.1 Hardware The experimental setup for the hand-held lance unit is as follows: The equipment was located with the computers required for the analysis mounted in an air-conditioned van that was driven close to the mine lanes. The high-pressure pump equipment, and contained water reservoir, were mounted on a pickup truck that was located at some distance from the lanes (Figure 28). This was to reduce the interference of the pump noise with the signals being detected at the lance. Water was provided from a 110 gallon tank, and, when the lance was not pulsing, this flow was recirculated back to the tank. It should be noted that given the hot nature of the day the insulated tank, and the small amounts of water used, the temperature of the water in the tank increased to the point of discomfort to the operator and this must be addressed in a future modification.

Figure 28. Overview of the test site where the mine detection was carried out. The high pressure pump

was located to the immediate right of the picture.

39

The experimental parameters used for data collection include: • Nozzle diameter: 0.050 inches • Water jet pressure: 2000-2500 psi • Tank microphone: 24 bit DAC • Floor microphone: 24 bit DAC • Video radar (large): 24 bit DAC • Pressure: 16 bit DAC • Sampling rate: 96 kHz • Microphone: Schoeps CCM41 supercardioid microphone2

The water jet nozzle angle was manually adjusted by the operator to account for local conditions. It generally varied between 30-45% from the vertical (Figure 29,30). The microphone was located above the nozzle on the lance and directed toward the expected water stream impact position. A more detailed discussion of this design is given earlier. microphone

jet Figure 29. Detail of the nozzle and components as used on site

2 http://www.schoeps.de/E/mk-ccm41.html

40

Figure 30. Wand in use at the test site

4.1.2 Data Acquisition and Graphical User Interface A National Instruments data acquisition board along with desktop computer running Labview was used as the primary data acquisition center. This was set up in the van that was taken to the field site. This board acquired one channel of microphone data and one channel of radar data along with operator comments etc. The primary purpose of this setup was to ensure a reliable data acquisition from the experiments conducted in the field test and to record them for future processing and evaluation. It was felt that such an elaborate data acquisition and processing center might not practical in operational mine detection system. Keeping this in mind a much simpler data acquisition system was developed on a Sony laptop. The microphone audio data and triggering were achieved through a National Instruments DAQ card. A graphical user interface was developed in MATLAB to manage the data acquisition, run various detection algorithms, report results and save the data for future processing. Figure 31 shows a screen shot of the developed user interface. The interface acquires the data, runs the detection algorithms and reports the appropriate response by illuminating either the red/green or blue color code in the lower left corner. Operator input can also be recorded for future reference.

Figure 31. A screen shot of the Graphical User Interface used for data acquisition and target detection.

4.1.3 Calibration Lanes Separate calibration lanes were provided for sand and for clay. In the sand and clay calibration lanes, there were 10 flags each of which marked a buried object, either a landmine or a harmless item, buried adjacent to the flag. An additional 10 flags were laid

41

out is a separate section adjacent to the calibration lanes. These items were not identified, and were to be used as a blind test of the system. UMR was merely informed that the objects were equivalent to some of the targets that had been used in the calibration lanes. Figure 32 provides the layout of the sand calibration and blind test lanes. The burial depth to the objects in the calibration lanes were 2” and 4” and are labeled in Table 6. The blind test lane consisted of objects buried at either 2” or 4” depths. At each flag location, either a harmless object or a landmine was present for both the calibration and blind test lanes. Figure 33 shows the layout of the soil/clay calibration and blind test lanes. For the local soil/clay separate 2” and 4” lanes existed. The flags labeled 1-5 for both the 2” and 4” lanes were calibration cases, with known objects or landmines present. The flags labeled 6-10 were blind test cases, with known depth and unknown objects or landmines present.

Figure 32: Layout for sand calibration and blind test lanes.

5 5 4 4 3 3 2 2 1 1 10 9 8 7 6 5 4 3 2 1

10 9 8 7 6 5 4 3 2 1 10 9 8 7 6 5 4 3 2 1

4” lane 2” lane

Blind Test Lane (2” and 4”) 4” 2” Calibration Lanes

Figure 33: Layout for clay calibration and blind test lanes. Flags 1-5 are calibration cases for the respective 2” and 4” lanes. Flags 6-10 are blind test cases for the respective 2” and 4” lanes. Table 6 presents the objects located at each flag position for the sand and clay calibration lanes. The flag number corresponds to the numerical label for the lanes provided in Figures 6 and 7 for the sand and clay, respectively.

42

Flag Number 2” Sand Calibration Lane

4” Sand Calibration Lane

2” Clay Calibration Lane

4” Clay Calibration Lane

1 OZM-3 M12 Metal Disc Metal Disc 2 PMN Plastic Disc Plastic Disc Plastic Disc 3 Wood Block PMD-6 PMN Wood Block 4 PMA-1A Metal Disc OZM-3 M12 5 VS-50 PMA-2 TS-50 PMA-1A Table 6: Object located at each flag position in 2” and 4” calibration lanes for sand and clay. Test runs in the sand and clay calibration lanes and with off-lane tests of the jet penetrating areas without a buried target were performed. These gave a total of 52 acoustic signals for objects as well as five signals for the clay soil only (no object) and three signals for sand only (no object)., The soil types were comparable to data obtained from prior data collections, eliminating the need to incorporate soil normalization before feature extraction from the acquired signals. Windowing of the acquired signals was based on skipping over the initial 0.25 seconds and retaining the samples over the following second for processing. There were 26 encounters from the clay and sand each. With only 10 flags available for calibration for each of the sand and clay types, more than one test shot was carried out at each flag. Table 7 show the number of different pulses directed at each of the objects in the calibration lanes. Note that some of the pulses did not hit the object. The number in the brackets shows the total number of pulses on each target, whereas the other number shows the number of proper pulses (pulses that actually hit the target).

OZM3 PMN PMD6 VS50 / TS50

PMA1A M12 PMA2 PPlate MPlate Wood

Cal Lane Soil 2”

2 (3)

2 (4)

_ 2 (3)

_ _ _ 2 (3)

2 (3)

_

Cal Lane Soil 4”

_ _ _ _ 2 (3)

2 (3)

_ 2 (2)

2 (3)

2 (3)

Cal Lane Sand 2”

1 (3)

3 (3)

_ 2 (4)

2 (3)

_ _ _ (2)

_ (1)

2 (2)

Cal Lane Sand 4"

_ _ 2 (3)

_ _ 2 (2)

1 (2)

2 (2)

2 (3)

_

Table 7: Number of pulses directed at the various mine/object types in each of the calibration lanes. Table 8 shows the number of pulses directed at the various targets in the test lanes. For the test lane we were not allowed to check if the pulse had actually hit the target, so these numbers below shows the total number of pulses made at each test target location (flag).

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Obj 1 Obj 2 Obj 3 Obj 4 Obj 5 Obj 6 Obj 7 Obj 8 Obj 9 Obj 10

Test Lane Soil 2”

_ _ _ _ _ 4

3

4

3

5

Test Lane Soil 4”

_ _ _ _ _ 5 6 4 6 6

Test Lane Sand

4 3 3 4 4 5 6 5 6 9

Table 8: Number of squirts on various targets in each of the test lanes.

4.1.4 Pre-processing and filtering of the Acoustic Signal When the waterjet is fired, a low frequency vibration is induced into the wand, due to the opening and closing of the waterjet valve. This vibration of the wand is picked up by the microphone due to its high sensitivity even at low frequencies. This signal was found to be additive so that we were able to filter away this contribution. A high pass 2048–tap FIR filter with a cut off frequency of 100 Hz was used for the purpose. Since typically there is no useful information in the frequency range of approximately 0 – 120 Hz, we were able to do this preprocessing without any loss of useful information. Figure 34 shows the plot of the raw signal and the plot of the same signal after high pass filtering. This filtering is clearly able to remove the unwanted low frequency vibrations in the signal.

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Figure 34: Plot of the raw signal before and after preprocessing.

(a) Raw Signal

(b) Raw signal in (a) after preprocessing

4.2 Application and Results Using WDD Approach (Calibration) Two approaches were investigated for extending the clutter model-based and landmine model-based classification approach. First, individual models were developed for sand and for clay based on developing a K-Means, nearest neighbor-based discriminator using the landmine calibration data. Second, a single model-based approach was performed to combine the clay and sand encounters into a single data set for model development. For the separate soil types, the initial step was to determine the cumulative histograms for the landmine encounters and for the harmless object encounters to find frequency ranges (bins) that provide discrimination capability for landmines and harmless objects. The cumulative histograms for landmines and harmless objects for both soil types appeared quite similar. For the separate soil types, a two-tiered classification scheme was examined. The first tier was based on K-Means clustering and nearest neighbor algorithms, as described previously, to develop a model to represent landmines. In this case there was no clutter model to be developed, so the output of the first tier of this classifier was to label all encounters (based on WDD features) with Euclidean distance to the nearest representative landmine cluster of less than or equal to a specified threshold as landmines. Otherwise, the encounter was passed to the second tier. In the second tier, clusters representative of landmines were determined based on the frequency bin features found from visual comparison of the landmine and harmless object cumulative frequency

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histogram bins. If the encounter had a Euclidean distance to the nearest representative landmine cluster (frequency bin features-based), the encounter was labeled as a landmine. Otherwise, the encounter was called a harmless object. This approach seemed appropriate because it addresses the need to avoid landmine misclassifications. Landmine models were developed using the FCM clustering to generate clusters representative of landmines using the frequency bin features. In order to evaluate this model, the entire set of landmine encounters and harmless object encounters were used in developing the WDD feature landmine model for the first tier and the frequency bin feature landmine model for the second tier. The two tier model was tested based on the training data. Preliminary results using this technique yielded poor landmine/harmless object discrimination. In fact, the frequency bin feature-based landmine model provided no improvement over simply using the WDD feature-based landmine model. This means that there was no improvement in classification results (landmine vs. harmless object) if the threshold applied for the WDD feature-based landmine model was used for landmine (less than or equal to the threshold) and harmless object (greater than the threshold) discrimination. Thus, subsequent analysis using the calibration data for sand and for soil was performed only using the WDD features. The next set of experiments compared classification results between training separate models for each soil type to combining the sand and clay data for training a single model for both soil types. For the separate models, the 2” and 4” sand calibration landmine encounters were used to train a WDD feature-based landmine model. Likewise, the 2” and 4” clay calibration landmine encounters were used to train a WDD feature-based landmine model. For the separate soil type encounter data, several encounters (squirts) were performed on the soil where no object was present, i.e. clutter encounters. From these clutter encounters, the mean and standard deviation were determined over the individually windowed clutter encounter data. Each sample within each windowed encounter for each soil type was normalized based on subtracting the mean and dividing by the standard deviation. WDD features were computed from the normalized windowed encounter data. For the combined soil type model, the sand and clay encounters were combined to generate one data set from which the WDD feature-based landmine model was developed. For the combined soil type, the means and standard deviations determined from the sand and clay clutter data were used for normalizing the respective sand and clay data. WDD features were computed from the normalized windowed encounter data. For evaluation purposes, all landmine encounters were used in the training the respective models. In testing the models, all landmine encounters and all harmless object encounters were run through the models to generate Euclidean distances to the nearest representative landmine cluster. Receiver operating characteristic curves were used to generate landmine and harmless object classification results. Thus, for these experiments, all harmless object encounters were independent testing data. Experimental results showed that the combined soil type yielded improved discrimination between landmines and harmless over the separate soil types. However, the overall landmine classification rates were poor. From the receiver operating characteristics (ROC) curve from the combined soil landmine model, setting the threshold to achieve 100% correct landmine recognition, yielded 27.7% correct harmless object classification. Inspecting

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the case of 62.0% correct landmine classification corresponds to 83.3% correct harmless object recognition. In analyzing the experimental results for the combined soil model, the classification rates for the first squirt at each flag location that hit the target were much improved over the remaining squirts or encounters. Specifically, from the ROC curve over the Euclidean distances to the nearest landmine cluster for the first encounter at each flag position yielded 92.3% correct landmine recognition with 72.7% correct harmless object recognition. Accordingly, the following approach was used for classifying the blind test encounters. The combined soil WDD feature-based landmine model was used. The single landmine model was applied to the clay and sand blind test lanes using the means and standard deviations computed for the sand and clay clutter encounters to normalize the windowed blind test encounter data. The same windowing approach was used as for the calibration data. The first encounter or squirt at each flag location provided the basis for the landmine/harmless object classification decision. In making landmine/harmless object classification decisions, the Euclidean distance to the nearest landmine cluster based on the combined soil calibration encounter data was thresholded using the same threshold as applied to the calibration data. If the Euclidean distance was less than or equal to the threshold, the encounter was labeled as a landmine. Otherwise, the encounter was called a harmless object. Using the landmine/harmless object distribution for the individual soil types and lane depths, the Euclidean distance from the respective soil type was compared to the Euclidean distances computed for the landmines and harmless object types found in the calibration lane. If the encounter was labeled as a landmine, the type of landmine assigned to the encounter was simply the landmine type from the calibration encounters with the closest Euclidean distance. If the encounter was labeled as a harmless object, the type of harmless object assigned to the encounter was simply the harmless object type from the calibration encounters with the closest Euclidean distance. Table 9 shows the results for the sand blind test lane using the flag labeling convention from Figure 32. The first column provides the flag number. The second column gives the landmine/harmless object decision from the WDD feature-based landmine model derived from the combined soil types. The third column shows the landmine/harmless object type. Table 10 shows the results for the 2” clay blind test lane using the flag labeling convention from Figure 33. The first column provides the flag number. The second column gives the landmine/harmless object decision from the WDD feature-based landmine model derived from the combined soil types. The third column shows the landmine/harmless object type. Finally, Table 11 presents the results for the 4” clay blind test lane using the flag labeling convention from Figure 33. The first column provides the flag number. The second column gives the landmine/harmless object decision from the WDD feature-based landmine model derived from the combined soil types. The third column shows the landmine/harmless object type.

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Flag Number

Predicted Landmine/Harmless Object Predicted Landmine/Harmless Object Type

1 landmine PMA2 2 landmine M12 (AT) 3 landmine VS50 4 harmless object PMD-6 5 landmine PMN 6 harmless object metal plate 7 harmless object plastic plate 8 landmine OZM3 9 harmless object wood block 10 landmine PMA 1-A Table 9: Sand blind test predicted results. Column 2 contains the landmine/harmless object decision. Column 3 provides the landmine/harmless object type. Flag Number

Predicted Landmine/Harmless Object Predicted Landmine/Harmless Object Type

6 landmine TS50 7 landmine PMN 8 harmless object plastic plate 9 landmine OZM3 10 harmless object metal plate Table 10: Clay 2” blind test predicted results. Column 2 contains the landmine/harmless object decision. Column 3 provides the landmine/harmless object type. Flag Number

Predicted Landmine/Harmless Object Predicted Landmine/Harmless Object Type

6 landmine PMA 1-A 7 harmless object metal plate 8 landmine M12 (AT) 9 harmless object plastic plate 10 harmless object wood block Table 11: Clay 4” blind test predicted results. Column 2 contains the landmine/harmless object decision. Column 3 provides the landmine/harmless object type.

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4.3 Application and Results Using HMM Approach (Calibration) In this section we summarize the training of the codebook and the HMMs for the HMM based mine detection using waterjet induced acoustic signal. Also representative results obtained for the calibration data from the field test are discussed. Results for the blind test at the test site are included in section 4.5. Our earlier analysis has shown that the discriminatory information is predominantly in the lower frequency spectrum of the waterjet induced acoustic signal. As a result we have down sampled the original 44.1 kHz raw data to a 6000Hz signal. This also makes the estimation of LPC/Cepstral coefficients less noisy and more representative of the desired signal. Up to 8th order LPC coefficients are used for the feature vector so that the resulting feature vector is 22 dimensional.

4.3.1 Codebook Design Since we used a discrete HMM with finite observation symbols, we needed a vector quantizer (VQ) to map each continuous observation vector into a discrete codebook index. Once the codebook of vectors had been obtained, the mapping between continuous vectors and codebook indices became a simple nearest neighbor computation. Thus the major issue in the VQ was the design of an appropriate codebook for quantization. After some trials we found a codebook size of 64 to be appropriate for this application. Note that we were working with a very limited dataset and thus a larger codebook size was not possible. Separate codebooks are designed for different soil conditions and different depths. To design the codebook, we selected an equal number of raw observation sequences corresponding to mines, objects and blanks respectively. Then the feature vectors for all these wav files were concatenated and passed on as a representative training sequence of vectors to a program that designs the codebook using K-means segmentation algorithm 23. Figure 35 shows the plot of the codebook obtained for the soil calibration lane 2” burial dataset. Each subplot shows the representative 22-dimensional template feature vector representing the characteristics of the corresponding symbol on the codebook. Similar codebooks were obtained for the soil calibration-lane 4” burial and the sand calibration-lane datasets. Note that the sand-lane is not classified based on depth as the ground truth for depth was not available for the blind data set from sand and thus the calibration data here were treated all together.

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Figure 35. Plot of the codebook designed for soil calibration-lane 2” burial dataset. After designing the codebook, the next step was to perform vector quantization on the feature vectors. This was achieved by calculating the distance of the feature vector from each of the vectors of the codebook. We assigned a given feature vector to that index of the codebook which it was nearest to, in the Euclidian sense.

4.3.2 Training of HMMs As discussed earlier a separate HMM was trained for each desired classification of the targets. A set of observation sequences from the calibration dataset corresponding to known pulses of the given class was used to train these HMMs. The following are the steps involved in the training of the discrete HMMs

1. The number of states in our model was kept fixed as 3 (we use N = 3). 2. The transition matrix and the observation matrix were randomly initialized. We

took care to make sure that all the probability matrices summed to one when summed along a row/column. The a priori probabilities of the states were initialized to forcing the condition that the HMM always started in state 1.

{ 001=Π }

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3. All pulses corresponding to the given class were selected and the corresponding observation sequence obtained. Given the limited dataset available, leave-one-out training and testing was not possible.

4. The quantized observation sequence was used to train the state transition matrix

and observation matrix starting from the randomly initialized parameters using Baum-Walsh method 21 .

5. Since the HMM parameter estimation may be trapped in local minima, we

performed the training routine many times (with different initial conditions) and chose the model that had the maximum mean likelihood ratio.

4.3.3 Mine No-Mine classification As discussed earlier, mine detection and classification was carried out at two levels. First the three-class problem was run, where each pulse result from the waterjet was classified as hitting either a mine, harmless object or just background material. The third class of background was used to account for the cases when the waterjet does not hit any object. Also separate HMMs were trained for the data from each calibration-lane. For each dataset of the calibration lane the pulses were classified as belonging to either background, mine or harmless object based on the available ground truth. Three separate HMMs were trained for each class and each dataset. After this stage, the HMM was tested on the dataset on which it was trained, to check if it had trained properly.

File Number

Mine/Object Type

Bad Bkg HMM

Mine HMM

Object HMM

001 MPlate n 0.33 0.36 0.65 002 MPlate n 0.34 0.39 0.66 003 PPlate n 0.56 0.25 0.68 004 PPlate n 0.36 0.32 0.65 005 PMN n 0.24 0.73 0.29 006 PMN n 0.24 0.71 0.27 007 OZM3 n 0.24 0.71 0.31 008 OZM3 n 0.24 0.69 0.27 009 TS50 n 0.25 0.68 0.42 010 TS50 n 0.24 0.73 0.26 011 Bkg n 0.65 0.26 0.48 012 Bkg n 0.65 0.25 0.50 013 Bkg n 0.71 0.25 0.35

Table 12: Likelihood values for each pulse fired in the soil calibration lane at a burial depth of 2” after the dataset had been passed through the three HMMs based classifier. Each dataset (such as the soil calibration lane with 2” burial) is trained and tested separately. For testing purposes for each pulse of the given dataset, the likelihood values were obtained for each of the three HMMs. These three values gave the likelihood of the signal belonging to the class represented by the HMM. The pulse is assigned to the class

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for which the likelihood is maximum. Table 12 shows the normalized likelihood values for each of the three classes obtained for the dataset obtained in the soil calibration lane at a depth of 2”. The second column shows the ground truth identity of the target. From the table we can see that the training has been accomplished very effectively. Each pulse is correctly classified based on these normalized likelihood values. However we may note that there is some ambiguity in the classification of background and harmless objects in the sense that the likelihood for the other class is also over 50%. For example in case of signal ‘003’ which corresponds to a hit over a plastic plate, while the likelihood that the pulse hit a harmless object is the highest at 0.68 the likelihood that the jet just penetrated background material is also significant at 0.56. This will suggest that it may be possible to confuse a harmless object with the background material or vice versa. Similar results were obtained for the dataset corresponding to the soil calibration lane at a burial depth of 4” and for the sand calibration lane data.

4.3.4 Target Identification After classifying the data into the three basic classes of mine, object and background, we proceeded to try and identify the target type (from among the seven mine types and three object types) present in each data class. In this case the signals from each dataset are classified based on their target identity and separate HMMs are trained for each target type for each of the three datasets. For the soil calibration lane data at 2” this results in 6 classes (4 mine types, one harmless object and background). Similarly for the soil calibration lane at 4” dataset we created 6 classes and the sand calibration lane data generated 9 classes. For testing purposes for each pulse in the given dataset, the likelihood values were derived for each of the trained HMM for the dataset. These values give the likelihood of the pulsed object belonging to the class represented by the HMM. The pulsed object is assigned to the class for which the likelihood is the greatest. Table 13 shows the normalized likelihood values for each of the six classes obtained for the dataset of the soil calibration lane at 2” for target identification. The second column shows the ground truth identity of the target. From the table we can see that the training produced a reasonable prediction. There is slight ambiguity in the classification of PMN, OZM3 and TS50 mine types where there is a chance of one being confused with the other in certain cases where the likelihood values for the two classes were not very well separated. This problem was more prominent in the other two data sets, corresponding to the soil calibration lane at 4” and the mixed burial depth data for the sand calibration lane. The problem would be less pronounced with a greater set of working data. Unfortunately this was the first time that some of these targets had been encountered and the conditions were therefore limited for the training.

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File Number

Mine/ Object type

Bad Bkg PMN TS50 OZM3 MPlate PPlate

001 MPlate n 0.41 0.31 0.27 0.40 0.74 0.41 002 MPlate n 0.39 0.26 0.43 0.27 0.70 0.39 003 PPlate n 0.57 0.24 0.23 0.27 0.31 0.72 004 PPlate n 0.40 0.28 0.34 0.37 0.38 0.67 005 PMN n 0.25 0.74 0.64 0.52 0.23 0.29 006 PMN n 0.27 0.72 0.60 0.50 0.28 0.27 007 OZM3 n 0.26 0.40 0.42 0.72 0.35 0.29 008 OZM3 n 0.25 0.50 0.48 0.69 0.32 0.30 009 TS50 n 0.24 0.24 0.73 0.23 0.48 0.30 010 TS50 n 0.23 0.72 0.72 0.44 0.22 0.26 011 Bkg n 0.70 0.26 0.23 0.25 0.29 0.59 012 Bkg n 0.67 0.25 0.27 0.27 0.35 0.46 013 Bkg n 0.77 0.25 0.23 0.29 0.49 0.40

Table 13: Probability values for each pulsed object in the soil calibration lane buried at 2”dataset obtained for the HMMs trained for target identification.

4.4 Application and Results using Maximum Likelihood approach (Calibration)

The calibration lanes were not only used to develop test statistics to identify objects in the blind test lanes, but were also used to refine the methods used in the maximum likelihood approach. Outside of the theory presented earlier, the maximum likelihood method may be improved by preprocessing the data or by using optimal groupings of the test data. Since our calibration data is limited, the ability to form larger groups that may be independent of one or more physical parameters (for example depth or soil type) may allow the formation of better test statistics. Three methods of preprocessing the data before application of the maximum likelihood approach were tested: a) normalization of the power spectral density such that the integral of power spectral density evaluated to one for each measured signal, Sr(f), b) taking the log of the power spectral density, and c) first normalizing and then taking the log of the power spectral density. Preprocessing of the data was compared to the case where no preprocessing was done. Several possible groupings of the data were tested. Groupings presented in this report include:

• Data were grouped according to soil type (sand, clay), object type, and depth. Objects were tested using the sand calibration data for all depths (since all depths were included in the blind test lane), in the 2 inch clay calibration lane, and in the 4 inch clay calibration lane. The search space included only those physical parameters (mine-type, soil-type, depth) that were likely to be encountered in a particular test. The percentage of objects correctly identified in this case is presented in Tables 14, 15 & 16.

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• Data were grouped together according to object type, regardless of the depth of the object or the type of soil the object was placed in. The percentage of objects correctly identified in this case is presented in Table 17.

• Data were grouped into only two classes, harmful or harmless, regardless of their more specific type (e.g. PMA vs M1A), the depth of the object, or the type of soil the object was placed in. The percentage of objects correctly identified as harmless or harmful is presented in Table 18.

• Objects were identified using a maximum likelihood approach for a group of shots over a single object, as described in section 2.3. Because of a lack of sufficient data, all available calibration data was used for both training and for testing. Results should still reflect performance of the technique reasonably accurately since data is represented statistically using only a few components. As the number of components is fewer than the number of calibration signals used to form the statistic, the approach cannot “memorize” the test set.

Sand lane Pre-processing method Percent correctly identified

No pre-processing 55% Normalize 18%

Log 0% Normalize and Log 36%

Table 14. Percentage calibration shots correctly identified in the sand lane. Depth was included in identification statistics. There were 11 distinct classes in this case.

2” Soil lane

Pre-processing method Percent correctly identified No pre-processing 80%

Normalize 60% Log 60%

Normalize and Log 80% Table 15: Percentage calibration shots correctly identified in the clay test lane with object buried 2” deep. There were 5 distinct classes in this case.

4” Soil lane

Pre-processing method Percent correctly identified No pre-processing 60%

Normalize 40% Log 20%

Normalize and Log 20% Table 16: Percentage calibration shots correctly identified in the clay test lane with object buried 2” deep. There were 5 distinct classes in this case.

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All test lanes Pre-processing method Percent correctly identified

No pre-processing 39% Normalize 57%

Log 17% Normalize and Log 30%

Table 17: Percentage calibration shots correctly identified in all test lanes, when sounds were grouped according to object type (regardless of soil type or depth). There were 10 distinct classes in this case.

All test lanes, harmless/harmful decision Pre-processing method Percent correctly identified

No pre-processing 59% Normalize 68%

Log 82% Normalize and Log 82%

Table 18: Percentage calibration shots correctly identified in all test lanes when objects were grouped as either harmful or harmless, regardless of soil type. There were 2 distinct classes in this case.

The maximum likelihood approach was able to distinguish harmless from harmful objects with reasonable accuracy, correctly classifying approximately 82% of the objects in the test set. However, since we were tasked with not only categorizing an object as harmless or harmful, but also identifying its type, this approach was not used to categorize the final test data. Grouping data only by object type did perform as well as grouping data by object and soil type and object depth. In the case where data were grouped according to object and soil type and object depth, best results were generally obtained without preprocessing. Based on these results, identification of objects in the blind test lanes was made by classifying objects according to the object-type, soil-type, and object depth without preprocessing data before application of the maximum likelihood approach. The objects in the blind test lane were first identified by type and then classified as harmless or harmful based on that type. In addition to the above tests where all shots over a single object were used to classify the object, tests were also performed where each individual shot was classified as striking a particular object or class of object. Using individual shots to identify the object did not perform as well as using all shots over the object, so those results are not presented here. The maximum likelihood approach requires several calibration shots for each possible set of physical parameters (object type, depth, etc) to accurately construct a test statistic. Its performance is crippled here by the small number of calibration shots we were able to acquire in the field test. A greater number of calibration shots would likely improve its ability to classify buried objects and would give a greater confidence in the presented measures of performance. Future work with more test shots is needed to substantiate the maximum likelihood’s ability to detect and classify buried objects.

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4.5 Blind test-lane results The three experimental signal processing approaches were used for object/no object and landmine/no landmine discrimination, as detailed in the previous sections. Table 19 presents the predicted landmine/harmless object and landmine/harmless object type results for the sand blind test lane as shown in Figure 32 from observer interpretation. Table 20 presents the predicted landmine/harmless object and landmine/harmless object type results for the 2” and 4” clay blind test lanes as shown in Figure 30 from observer interpretation. Table 21 shows the predicted results for the sand blind test lane using the clustering landmine model-based approach based on WDD features. Table 22 shows the predicted results for the 2” and 4” clay blind test lanes using the clustering landmine model-based approach based on WDD features. Table 23 gives the predicted results for the sand blind test lane using the hidden Markov model and LPC feature-based approach. Table 24 gives the predicted results for the 2” and 4” clay blind test lanes using the hidden Markov model (HMM) feature-based approach. Tables 25 and 26 give the predicted results for the sand blind test land and for the 2” and 4” clay blind test lanes, respectively, using the Maximum-Likelihood spectral-based approach. The accuracy of these predictions is not currently known to UMR. Observer Interpretation Flag Number Predicted Landmine/Harmless Object Predicted Type

1 Landmine PMA-2, OZM3 2 Landmine M12 (AT), Plate 3 Landmine PMN, OZM3 4 Landmine PMD-6, OZM 5 Landmine VS50 6 Harmless object Plate, M12 (AT) 7 Harmless object Plate, PMA-1A 8 Landmine OZM3 9 Harmless object Wood Block 10 Landmine PMA-1A, PMD-6, Plate

Table 19: Observer interpretation for sand blind test lane. 2" Lane Observer Interpretation Flag Number Predicted Landmine/Harmless Object Predicted Type

6 Landmine TS50 7 Harmless Object Plate 8 Landmine PMN, OZM3 9 Landmine OZM3, TS50 10 Harmless Object Plate

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4" Lane Observer Interpretation Flag Number Predicted Landmine/Harmless Object Predicted Type

6 Landmine PMA-1A 7 Harmless object Plate 8 Harmless object Wood Block 9 Harmless object Plate 10 Landmine M12 (AT)

Table 20: Observer interpretation for the 2” and 4” clay blind test lanes. Flag Number

Predicted Landmine/Harmless Object Predicted Type

1 landmine PMA2 2 landmine M12 (AT) 3 landmine VS50 4 harmless object PMD-6 5 landmine PMN 6 harmless object metal plate 7 harmless object plastic plate 8 landmine OZM3 9 harmless object wood block 10 landmine PMA 1-A Table 21: Sand blind test predicted results using cluster landmine model-based approach based on WDD features. 2" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 landmine TS50 7 landmine PMN 8 harmless object plastic plate 9 landmine OZM3 10 harmless object metal plate 4" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 landmine PMA 1-A 7 harmless object metal plate 8 landmine M12 (AT) 9 harmless object plastic plate 10 harmless object wood block

Table 22: Clay 2” and 4” blind test predicted results using cluster landmine model-based approach based on WDD features.

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Flag Number

Predicted Landmine/Harmless Object Predicted Type

1 harmless object Wood 2 landmine VS50 3 landmine OZM/VS50 4 landmine PMN 5 landmine PMD6/PMN 6 harmless object PPlate 7 landmine OZM 8 landmine PMN 9 landmine PMA-2 10 harmless object PPlate Table 23: Sand blind test predicted results using the HMM and LPC feature-based approach. 2" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 landmine PMN/TS50 7 harmless object MPlate 8 landmine OZM3 9 landmine PMN/TS50 10 harmless object PPlate/OZM3 4" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 landmine M12 7 landmine PMA1A 8 harmless object PPlate 9 harmless object MPlate 10 harmless object Wood

Table 24: Clay 2” and 4” blind test predicted results using the HMM and LPC feature-based approach.

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Flag Number

Predicted Landmine/Harmless Object Predicted Type

1 landmine VS50/M12 2 landmine VS50/M12 3 landmine VS50/M12 4 landmine VS50 5 harmless object PPlate 6 harmless object MPlate 7 harmless object PPlate/PMA1A 8 harmless object PPlate 9 harmless object PPlate 10 landmine VS50 Table 25: Sand blind test predicted results using the Maximum-Likelihood spectral-based approach. 2" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 landmine OZM3/PPlate 7 landmine OZM3/PPlate 8 landmine OZM3 9 landmine OZM3 10 harmless object Background (no object) 4" Lane Flag Number Predicted Landmine/Harmless Object Predicted Type

6 harmless object Wood 7 harmless object Wood 8 harmless object Wood 9 harmless object Wood 10 harmless object PPlate

Table 26: Clay 2” and 4” blind test predicted results using the Maximum-Likelihood spectral-based approach.

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5 Identification Approach In order for the demining team to identify an object, and to decide on the appropriate response, the mine has, historically, been uncovered. This has generally been done manually, and can take some time and pose some risk to the individual. To overcome these drawbacks a light-weight means for removing the soil from over the mine has been proposed. The unit operates from the same pump as that used for the detection system, and so that hardware will not be described at this time, but only the equipment that will be used at the other end of the high-pressure hose. Given that the change in these tools (from detection to soil removal) can be made by the detachment of a quick-connect fitting at the tool:hose interface and then its reconnection, this is a rapid, and simple method for utilizing the same equipment for multiple functions.

6 Identification Hardware Identification Approach: The mine identification tool is based on a concept of using a stream of high-pressure water to separate out the individual particles/components of a soil and move them in a low –friction environment into the mouth of a suction device. That device is then able to rapidly remove them from the area, with little additional disturbance outside the immediate zone of action. The pressure, flow-rate and standoff, at which the nozzles operate, are controlled by the hardness of the soil, and penetration parameters are set at each site. The intent is to remove between half and one inch of soil on each pass over the ground, since the depth to the object is not initially known. The tool is designed for remote control so as to minimize risk to the operator. The choice of aspirating the soil from the site, rather than using the water to move it away was selected because of the additional control that it gave to the walls of the excavation (see later figures), the ability to recover the water where this might be an issue, and the removal of the risk that the mine might be projected into an area that had previously been cleaned, where it might be covered and create a new risk. The approach is based upon the need for a simple tool that can cope with widely varying ground terrain and type, and that an individual can easily position and operate. Further the tool should be relatively easy to maintain. Identification Hardware: Initial Prototype I For the initial laboratory validation of the proposed method, a prototype apparatus was built. In this first approach, the soil sucker prototype was constructed of aluminum and stainless steel. (These construction materials can be replaced with either molded plastic parts or wood to decrease the overall weight and make the equipment more easily repairable in the field). The apparatus weighed approximately 24 pounds and measured 35”L x 12”W x 12”H. The overall height is an approximation because this was designed to be adjusted. The soil removal tool (SRT) provided 18” of horizontal travel for the nozzle assembly. (Figures 36 and 37). The SRT was connected to the high-pressure pump through a quick connect fitting. For the purpose of the initial tool development, however, a second, smaller pump was used to provide the water flow. This was a North Star

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pressure washer that supplied the necessary water at pressures of up to 3,500-psi at a flow rate of up to 4 gpm. The suction tube outlet from the SRT was connected to a standard machine shop wet/dry vacuum. unit The unit used 6.25 peak HP and can hold 22 gallons of material.

Figure 36: Soil Removal tool detail Figure 37: Overview of initial prototype

Two end plates and four guide rods held the basic frame of the tool together. The stainless steel rods also served as a guide for the carriage assembly. The SRT was mounted to the carriage assembly through a pivoting support head, which allowed three axes of freedom in the location, positioning and orientation of the nozzle assembly. The carriage moved back and forth along the guide rods under power from a 12V DC motor and pulley system. The motor shaft was connected to a small gearbox and the drive shaft in turn connected to a larger spool. Wire coiled around the spool moved the carriage as the spool rotates. Plastic castors guide the wire on the corners of the apparatus. A small turnbuckle removed slack in the system. Bushings made of cast nylon (from Nylatech, Inc.) supported the carriage as it moved. Some of these features are shown in Figure 36, which shows the SRT at one end of the stroke with the turnbuckle for tightening the drive wire visible, and the pulleys that direct the wire.

Figure 38. Artist’s rendition of a section of the SRT showing the internal components

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The SRT utilized a self-rotating nozzle (StoneAge, Inc.) to provide rotary force to move the nozzles around the suction tube. High-pressure water entered through a quick connect fitting from the high-pressure hose. A vacuum port was located on the side of the apparatus. As the water jets penetrate the ground, the loose soil and water was washed into the collection tube and drawn out through the vacuum port. Handles on either end plate were installed to provide an easy way to carry the apparatus. The apparatus’s feet were designed to be adjustable, to allow for differences in the contour of the ground.

Figure 39: Initial SRT head at low jet pressure, note that secondary nozzles had been placed with each cutting jet to improve the drive force and provide self-rotation for the head. The SRT was tested at pressures up to 5,000 psi with three nozzles of .031” and three propelling nozzles of .026” (Figure 39). The propelling nozzles were oriented to provide thrust to rotate the head. As a self-rotating assembly, the cutting head initially rotated too quickly to effectively cut through grass roots. In addition the device was liberating and removing soil faster than the suction tube could extract it. This was partly caused by centrifugal force induced by the initial high speed of rotation of the suction tube and nozzles. To overcome this problem it was decided to positively drive the rotation of the head use a small DC motor to rotate the head instead of using the propelling jets. The advantages of this change will allow varying the rotational speed in a wide range, in a controllable manner. By eliminating the propelling jets, the amount of water will be significantly reduced. By using a small motor, the amount of mechanical energy imparted to the ground or a mine would be small, and any friction generated would stop the rotation without generating sufficient force on the mine to induce reaction.

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Second Prototype II The amount of water used by this tool plays a very large part in its successful operation. While conversion from a self-propelling device to a driven unit will reduce the amount of water needed and provide a means of controlling the rotational speed, it reduces the amount of water available to dislodge the soil. Thus a careful balance had to be sought to change the rotational speed of the water jet nozzles and provide sufficient residence time of the waterjet over the surface, to achieve a controlled waterjet depth of penetration. The cutting depth is a therefore controllable, and provides an additional effectiveness of this method. A small 24VDC motor was built into the assembly to provide rotation, through a simple gear drive from the motor to the assembly housing (Figure 40). The initial gears shown were made of plastic, however a short initial test period showed that these were too weak and they were replaced with metal gears. Also a protective cap was added to provide protection of the gear drive system and rotating nozzle from debris generated during soil removal.

Figure 40: 24V DC motor attachment Figure 41: Rubber bellow attachment, with nozzle mounts

A problem was noticed when testing the first soil sucker prototype. It was unable to pick up all of the debris from the bottom of the trench created by the rotating water jets. Adding a flexible element at the end of the tube can, to a degree, reduce the need to accurately control the distance between the rotating suction tube and the surface of the ground. It would also reduce the forces applied to the ground or any mine present, since the housing would deflect, rather than transmit great force. The rubber bellows used was 3" long and was attached to the tube, as shown in Figure 41. In order to determine the practicality of the new tool small simulated mines (red objects) were constructed and buried in trays containing a variety of soil types. This allowed, for example, clay to be used as the burial soil, but the clay could then be heated in an oven to simulate the baked clay environment that might be encountered in various parts of the world. The new configuration of tool was demonstrated to Ft Belvoir personnel in at demonstration held at the University of Missouri-Rolla on May 3, 2002 (Figures 42, 43).

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Figure 42: SRT test on clay Figure 43: Test on soil, note the stability of the cut walls.

The current design of the SRT was then used to successfully demonstrate that it could uncover simulated landmines in multiple environments. These tests included uncovering simulated mines buried in sand, soil, and baked clay. The tool also was able to remove roots and grass as well as soil, when tested in that environment (although no mines were buried in these tests the result from one of which is shown in Figure 44).

a) starting to cut b) depth of cut achieved

Figure 44. Tests of second generation system on grass, the slot was at the width of the SRT.

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Soil Sucker Prototype III Although the SRT head had shown that it could achieve the results required, there was a concern that the support platform would have problems working in more difficult terrain. A new design was therefore developed in which the SRT was mounted on a tripod assembly (Figure 45) instead of the linear track mount. The tripod provides a strong support to the soil sucker assembly and provided adjustable legs with a much greater range to adjust to significantly uneven terrain. The tripod is also collapsible and lightweight for ease in transportation. In addition, by including drive motors on the normal elevation and rotation drives and adding a third lateral feed to the main support brace (Figure 46) the tripod assembly was built to allow the SRT to move with three axes of freedom. By motorizing the vertical height adjustment to give vertical movement the SRT could be programmed to make a pass over the surface, lower an increment, and then make a return pass, successively stripping the soil from the mine in small layers to minimize surface loading. To move horizontally over the mine location, the pivoting movement was motorized, driving the arm in an arc sweep over the work area (Figure 47). This sweeping action is achieved by pulleys and a timing belt that turns the arm that holds the soil sucker (Figure 46). To change the sweep radius, this arm is fitted with a motorized telescopic assembly. This consists of a rack and pinion assembly, with 12-volt DC motors providing power to the three axes of movement.

Figure 45: Tripod mount for the SRT Figure 46: SRT tripod arm

To control each of the various motors in the tripod assembly a simple remotely-operated control box was constructed (Fig. 48). The vertical and telescopic motions of the tripod are controlled by the toggle switches on the right of the box. The sweeping motion is also controlled by a toggle switch, connected to a variable speed controller for that particular motor.

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Figure 47: Sand/clay test showing path Figure 48: SRT control box

The effectiveness of the new design was again validated by making cuts over a grassy area at UMR (Figure 49).

Figure 49: Sample passes over grassy terrain to validate the motion controls of the new prototype.

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The new soil removal tripod assembly was tested on various soil samples, as well as on the grass covered terrain. No problems were identified in running these tests, for soil samples that had high concentrations of sand, it was only necessary to make 1 or 2 passes before the mine was uncovered (Figure 47). When the tests were carried out on the grass-covered soil, however, multiple passes were required to reach a 6-inch depth, because of the need to remove all the roots and vegetation with the soil (Figure 49).

7 Identification in the Field Seven objects were buried in a test lane at the demonstration site (Figure 50). The identity of the objects were not initially provided, however the relative location of the object to the identifying flag was. For ease of initial operation the support vehicle that carried the high-pressure pump was brought close to the operation, although the system could have been carried out, if necessary, at a greater distance (Figure 51).

Figure 50. Test lane before soil removal Figure 51. Equipment used for uncovering mines. The tripod was set up so that the flag sat on the edge of the arc of movement, and centered along it. An initial demonstration was made on soil that had been packed during normal site preparation and not then disturbed to lay the objects. (With no rain between the time of installation and the time of test the ground had not fully re-compacted over the objects). In the initial passes the hose entry to the suction line was blocked by a single large piece of rubber that was aspirated into the tube during the operation. This was removed and the tripod set up over the first mine. Two passes were required to uncover the edge of the mine, which protruded from the side of the excavation (Figure 52), at the request of the Project Officer the arm was then extended and subsequent passes made over the top of the mine, to determine if the action of the jet on the rubber coating would do any damage. The arm was held stationary over the mine while the jet struck it (Figure 53) without doing any discernable damage. The mine was left exposed.

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Figure 52 uncovering the first mine Figure 53. Continuous impact on the mine after It had been exposed. There was still a problem with pebbles lodging in the throat feeding from the SRT to the suction hose, but despite this the tripod was moved to a second flag and a second mine exposed (Figure 54). The continued blockage of the hose by the pebbles, which were larger in size than had been anticipated, led to a change, initially to a stiffer hose. When the soil was removed from the following two mines, this solution was not found to be adequate since the pebbles were still blocking in the entrance passage to the hose, reducing the suction force and lowering the performance below achievable levels. A small wire screen was therefore built across the moth of the rubber feed tube, with a small enough mesh that it would block the passage of the pebbles into the line. This proved to work satisfactorily and the final three objects were exposed with no additional problems.

Figure 54. Exposure of the second mine, note the relatively straight walls of the excavation achieved.

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It was interesting to note that the small AP mine, used as object number 6 had a small pebble sitting on top of the mine as it was excavated. The process of removing the soil from around the mine was sufficiently gentle that this pebble was not moved, but held in place after the soil had been removed and the top of the mine had been uncovered. Although the mine could have been cleared to a greater depth this feature of the excavation was felt sufficiently noteworthy that the mine was left in that condition (Figure 55). Photographs of the excavated items are given below (Figures 55 – 62). Video records of each mine exposure are available from UMR. Generally the clearance took on the order of one minute to achieve after set-up. After all the mines had been excavated a second test run was made on an adjacent section of undisturbed ground, with the cut going down to the depth of the rubber shroud without the system experiencing any difficulty in removing the soil or screening the pebbles to ensure that the suction line was not blocked during the operation.

Figure 55. Mine 6 – note the pebble Figure 56. First mine exposed (2 days later)

Figure 57. Second mine exposed (2 days later) Figure 58 Third mine exposed (showing wires)

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Figure 59 Fourth mine exposed (1 day later) Figure 60 Fifth object exposed (wood being

uncovered)

Figure 61. Fifth object after test Figure 62. Seventh mine after test Although there are a number of small details that require additional effort – the screen that was improvised needs to be built in a better fashion and located more appropriately, and the aspirating rubber tube and nozzle configuration need to be adjusted to allow a deeper penetration into the soil, the general operation of the SRT – the Soil Removal Tool – was considered to be successful during this demonstration. All objects were uncovered without damage using a relatively rapidly and easily deployable system, which had the capabilities of also powering the detection and neutralization phases of the program.

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8 Neutralization Approach The standard method for dealing with landmines in the field is to place a donor charge of explosive on the mine and then to explode it in place. For a variety of reasons (such as the amount of collateral damage thus induced) this may not always be appropriate. In addition the structure of the mine may be such that it is difficult to gain access to the contained explosive within the mine to ensure that it is detonated when the donor charge is fired. In alternative approaches, small propellant charges may be used to cut through the casing and induce reaction in the contained explosive. However, again, it is possible that the casing configuration may be such that the contained explosive is not completely dealt with. An alternative approach is proposed where the high-pressure pump and support system, have a pair of pressure vessels included in the overall assembly (Figure 1). By including these cylinders, and a way of filling them with abrasive, it is possible to inject a controlled volume of abrasive into the flow of water passing from the pump to the nozzle (Figure 63). This abrasive mixes with the water to form a thin slurry that ejects from the nozzle with sufficient velocity that it forms a cutting stream. The cutting stream is sufficiently powerful that it can cut through the casing and contents of the landmines that it might be put up against. In initial proof testing of the concept such an abrasive slurry jet (ASJ) was able to cut horizontally through an inert anti-tank (AT) mine with all the fuze wells and steel walls that this contains (Figure 64).

Figure 63. Schematic of ASJ abrasive circuit. Figure 64 Sectioning an inert AT.

9 Neutralization Hardware Neutralization initial development The pump and supply system to generate the high-pressure fluid flow to the cutting head can be the same as those used to power the detection and identification missions of the program. Early in the program it had been anticipated that it would be necessary to use a higher-pressure pump (at 10,000 psi) and an abrasive system of the size appropriate to that pressure. One of the early developments of this program was, however, an alternate configuration for a pump and abrasive injection system that would allow the use of a lower pressure. To facilitate and control the flow of abrasive from the pressure vessel, a

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bladder was interposed between the inflowing water at the bottom of the cylinder (see Figure 63) and the contained abrasive. As this bladder filled, it displaced the abrasive at a steady concentration into the water line to the nozzle. However the flow of abrasive could be decreased by inter-particle friction. To overcome this, a long-chain polymer (commercially known as Superwater) was mixed with the abrasive. This provides a friction reducing role and allows the abrasive to flow easily from the vessel. At the same time the polymer provides an improved cutting rate because of its cohering action once the jet leaves the nozzle. When using the combined system it was anticipated that the system would perform adequately to meet the identified need. For the purpose of this development this was defined as being able to cut 0.5 inches through steel.

Neutralization Approach Once the system for generating the abrasive flow had been identified, the major item in controlling its effectiveness is the tool that controls the cutting path. The abrasive jet will cut through this thickness of metal at a traverse speed that must be kept in the low inches/minute rate. Such a speed cannot be accurately manually controlled. At the same time there are perceptual risks with having an operator manually move the device across an explosive-laden item. A remotely-operated but precise manipulation system was required. The requirement was also that the tool had to cut from within the channel that would be cut alongside the mine by the SRT. This channel is on the order of 4-inches wide and could be around 6-inches deep. The device had to be manually carried and emplaced at the mine site, which could be in difficult terrain. For these reasons it was decided to adopt the concept of a tripod-mounted system similar to that used for the Soil Removal Tool. The particulars of the mounting and drive would, however, need to be changed to accommodate the different mission of this tool. The adjustable tripod gives the Mine Cutting Tool (MCT) the ability to be correctly aligned to make the desired cut when set on a range of different terrain (Figure 65). The mounting bracket for the MCT as built from aluminum arm that supports two bearings, which hold the rotating cutting nozzle. Two cog pulleys and a timing belt drive the rotation of the nozzle, powered by a 24V motor (Figure 66). A second, 12V, motor is mounted on the side of the tripod and this allows the arm to be raised and lowered vertically. A control box was purpose built to control each of the motors and to vary their speed, and that of the nozzle. The initial system was built, tested and demonstrated to NVESD personnel on their visit to Rolla in May, 2002. For the purpose of that demonstration the tool was used to make both a vertical and a horizontal cut in a steel plate, 0.25 inches thick (Figure 67, 68).

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Inlet water hose connection

nozzle rotation drive motor

Figure 65: Overview of the Tripod and MCT Figure 66: Modified cutting head mount

Figure 67 Testing the MCT at UMR Figure 68 detail showing the small water flow. Although the mine cutting system was successful in these tests, modifications were made to increase the stability of the cutting head. A second bracket with bearing was attached, so that this dual bearing assembly would eliminate any slight vibration or wobbling of the cutting head. Rubber covers were fitted to protect the motor and bearings from water and grit.

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10 Neutralization in the Field It was not felt appropriate to cut live or inert mines during the demonstration of the technology at the field test site. For this reason an alternate target was selected. A large metal plate was located at the site and permission was obtained to make a cut into this surface. In this way the tool would demonstrate a depth of cut that could be achieved, rather than cutting all the way through a surface, where the actual potential depth of could would not be as apparent. The equipment was set up and driven from the truck-mounted high-pressure pump with the connection made by quick-disconnect to the MCT mounted on the tripod (Figure 66). The polymer was mixed with the abrasive (due to temperature sensitivity of this particular polymer its carrying capacity was potentially reduced by the record high temperatures encountered at the test site on the day of the test). One of the cylinders was loaded with abrasive and the nozzle located adjacent to the steel plate (Figure 69). The jet was then brought up to pressure and traverse down the steel (Figures 70,71).

Figure 69. Bringing the jet to pressure Figure 70. Making the test cut.

Figure 71 Cutting detail Figure 72 Cut after the test

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The cut was made over a period of two minutes and was also videotaped. Because of the thin nature of the slot cut (Figure 71) the depth could only be measured by inserting a narrow wire into the slot and measuring the length indirectly by that means (Figure 73).

Figure 73 Depth of cut achieved Figure 74 Overview of equipment The experiment demonstrated that the equipment that had been brought to the test site, and which can be fielded from a single pickup truck (Figure 74) could be used with three different tools, each easily connected to the high-pressure pump, to either detect the presence of a mine, uncover a suspicious object to determine its nature, or cut through over one and a half inches of steel to segment the mine, either to neutralize the fuse or to expose the contained explosive for an alternate treatment.

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11 Review and Conclusions The purpose of this program has been to develop the equipment and technologies to demonstrate that high-pressure waterjets have the potential to detect, identify and neutralize landmines buried in the ground. The detection of the mine presence was achieved by the analysis of the acoustic signal generated where a short pulse of high-pressure water was fired into the ground. Distinct differences in the sound measured were discerned and these signals were analyzed to identify the objects that were detected. No. Soil & Burial

Observer Prediction

WDD Prediction

HMM Prediction

Max. Like Prediction

Actual

Sand mixed 1 PM2-A,OZM3 PMA2 Wood VS50/M12 2 M12, Plate M12 (AT) VS50 VS50/M12 3 PMN, OZM3 VS50 OZM3/VS50 VS50/M12 4 PMD-6,OZM3 PMD-6 PMN VS50 5 VS50 PMN PMD6/PMN Pl. Plate 6 Plate, M12 Metal Plate Plastic plate Metal Plate 7 Plate,PMA-1-

A Plastic plate OZM3 Plate/PMA-1-

A

8 OZM3 OZM3 PMN Plastic plate 9 Wood Wood PMA-2 Plastic plate 10 PMA-1-

A/PMD-6/Plate PMA-1-A Plastic plate VS50

Soil 2 in burial 6 TS-50 TS50 PMN/TS50 OZMS/Pl Plate 7 Plate PMN Metal Plate OZMS/Pl Plate 8 PMN/OZM3 Plastic plate OZM3 OZM3 9 OZM3,TS50 OZM3 PMN/TS50 OZM3 10 Plate Metal plate P Plate/OZM3 No estimate Soil 4 in burial 6 PMA-1-A PMA-1-A M12 Wood 7 Plate Metal plate PMA-1-A wood 8 Wood M12(AT) Plastic plate Wood 9 Plate Plastic plate Metal Plate Wood 10 M12 (AT) Wood Wood Plastic plate However it should be noted that the calibration data collected for the field test was quite limited. As a result the training of the detection algorithms could not be achieved with a

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high degree of confidence. More testing and training of different detection algorithms is required in order to further develop and demonstrate the effectiveness of these detection algorithms. In the course of the demonstration of the equipment it was noted that the soil/sand response to the jet impact also varied with the buried object and the depth of burial, so that the operators of the equipment were able to make educated estimates of the nature and depth of unknown items during the test procedures. This column of estimates is added to the table of overall estimates as to the nature of the objects buried in the unidentified sections of the test lanes. This is a potential method of use that had not been anticipated at the start of the test series and provides an additional avenue for further research in the use of this tool. In addition to the potential for finding the mine, there is a need to uncover it so that it can be identified. Seven objects were uncovered, and the gentleness of the process is perhaps illustrated by the presence of a pebble, partially buried in the excavation wall, that was left undisturbed as the mine was uncovered. All of the objects were revealed and could be examined. The walls of the excavation were stable, and the majority of the water used in the relatively short (about a minute) time that the object was uncovered was captured and could have been separated for reuse. The tool was simple to assemble, locate and manipulate. In the third part of the program, abrasive was added to the fluid of the high-pressure jet and a third tool used to manipulate this over a target. The tool demonstrated that it could readily cut to a depth of over 1.5 inches with a simple manipulator that could be used to either cut vertically or horizontally over a target. The three parts of the technology have therefore been given an initial field test. To become more accurate more data is required to refine the accuracy of the predictive models used to identify the targets. In addition some considerable work is required to make the equipment easier to use, more reliable and robust. Based on the results from the first field test program, it is recommended that that development be made.

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12 References 1. D.A. Summers, Disintegration of Rock by High Pressure Jets, PhD Dissertation,

Mining Engineering, University of Leeds, UK, 1968. 2. D.A. Summers , Waterjetting Technology, E & FN Spon, London, 1995, 882 pp. 3. R. Denier, M.S. Thesis, University of Missouri-Rolla, 1999. 4. Rinker et al CSEE paper 5. ANS paper reporting Oak Ridge success 6. Paul’s first paper on ASJ cutting 7. Miller on Crane cutting munitions 8. ALBA paper on cutting EOD 9. J. A. Stuller, S. J. Qiu, K. Das, “Signal Processing for Landmine Detection Using a

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25. Paul.D.Gader, Miroslaw Mystkowski, and Yunxin Zhao “Landmine Detection With Ground Penetrating Radar Using Hidden Markov Models”, IEEE Transactions on Geoscience and Remote Sensing, vol.39, no.6, June 2001.