detection of obscured targets: signal processingpeople.ee.duke.edu/~lcarin/mcclellan8.3.05.pdfgpr...

Detection of Obscured Targets: Signal Processing

James McClellan and Waymond R. Scott, Jr. School of Electrical and Computer Engineering

Georgia Institute of TechnologyAtlanta, GA 30332-0250

[email protected]

MURI Review 3-Aug-05 Scott/McClellan, Georgia Tech 2

Outline

Introduction

Location with Acoustic/Seismic ArraysManeuvering Array (3x10)

Cumulative Array strategy for imaging

GPR and QuadtreeRegion Elimination

Accomplishments/Plans


Three Sensor ExperimentSensor Adjustments and Features

Adjustable Parameters for all three sensors

Frequency rangeFrequency ResolutionSpatial ResolutionIntegration time/bandwidthHeight above ground

Location

Possible Features for sensorsEMI

Relaxation frequencyRelaxation strengthRelaxation shapeSpatial response

GPRPrimary ReflectionsMultiple ReflectionsDepthSpatial Response

SeismicResonanceReflectionsDispersionSpatial response


Multi-Resolution Processing

GPR

EMI

Seismic

Imaging

SigProc

Imaging

Features

Features

Features

DecisionProcess

ExploitCorrelation

Training

Detect

Classify

ID

Quadtree Imaging@ increasingResolution(Eliminate Areas)

Target Localization@ specific sites

Ad-HocArray


Outline

Introduction






Seismic Sensor

Radar:R.F. Source,Demodulator, andSignal Processsing

Signal Generator

Elastic WaveTransducer

ElasticSurfaceWave

Mine

E.M. Waves

AirSoil

S N S

Wav

egui

de

Displacements


Ground Contacting Seismic Sensor Array Deployed on a Small Robotic Platform

Source Platform

Sensor Platforms


Home in on a Target

Problem Statement:Use a small number of source & receiver positions to locate targets, i.e., landmines

Minimize the number of measurements

Three phases1. Probe phase: use a small 2-D array (rectangle or cross)

Find general target area by imaging with reflected waves

2. Adaptive placement of additional sensorsManeuver receiver(s) to increase resolution

Use “Theory of Optimal Experiments”

3. On-top of the targetEnd-game: Verify the resonance


Adaptive Sensor Placement

Probe ArrayProbe Array findsgeneral target area

1

2

3Additional sensors are added to the “cumulative array”

Target

Source

On-top forresonance


Probe PhasePick five sensors in a cross patternApply Imaging algorithm and plot the surface over a search grid

Maneuver PhaseAdd one more sensor depending upon which direction to moveIncrease aperture of triangulationSpatial resolution increases which narrows down the area in which to search for target

Review from January


TS-50 (1cm)

Source at(-20,50)


1

2

3

Next Measurement


Want to formulate an optimal maneuvering strategy

Instead of adding One Sensor, move the Whole Array to the next optimal position

Append the array data at the new position with previous data

Estimate the target location

Find the “best” next sensor position

Repeat this until target is localized

Choose Next Sensor Position


Steps in Optimal Maneuver


Wave Separation by Prony

Imaging algorithm

- Data Model

- ML solution for target position estimates

- Performance bounds for position estimates

Next optimal Array Position

- D-optimal Design

- Constrained Optimization

Steps in Optimal Maneuver


P-element array with position of every sensor is given in terms of array center Seismic wave penetration depth depends on frequency, hence a band of frequencies is used in processingK near-field wideband targetsIn the DFT, select ‘L’ Frequency components

Goal: estimate location (p) of the targets from measured array data

Data Model


Data Model


Target Position Estimate

The Cramer-Rao lower bound (CRLB) provides a lower bound for the variances of the unbiased estimators

CRLB requires the inverse of the Fisher information matrix


Fisher Information Matrix for Position Estimate


The theory of optimal experiments uses various measures of the Fisher information matrix to produce decision rules

Determinant

Trace

Maximum value along the diagonal

D-optimal Design uses the determinant

X. Liao and L. Carin, “Application of the Theory of Optimal Experiments to Adaptive Electromagnetic-Induction Sensing of Buried Targets,'' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26 , no.8, pp. 961−972, August 2004.

Theory of Optimal Experiments


Next Optimal Position


Target position is estimated from reflected seismic energy

Source is nearbyNeed separation between forward and reverse waves

Array position has to be between source and targets always

Landmines reflections are not “omni-directional”

Next array position has to be Constrained“Between” source and estimated target positionTwo ways to implement constrained optimization

Circle constraint, or Penalty Function

Unique Problems for Seismic


Constrained optimization for next array position


Single TS-50 landmine is buried at 1cmReflected waves are separated at each position by using a line array of 15 sensors (3cm apart)

Actual receiver is a single sensor (Synthetic Array)Measurements are grouped into line arrays

From each line array, three sensors at equal distance positions are chosen

With three line arrays, an array of nine sensors is available for 2-D imaging

Inverse of the cost function is plotted for imaging algorithm

Experimental Data Setup


Seismic Detection of VS-1.6 AT Landmine Buried 5cm Deep in Experimental Model

Landmine (22.2 cm diameter) buried 110 cm from first measurement location in 171 cm linear scan.

5 measurements with center line of sensor array along a line over the burial location.

Compressional, Rayleigh Surface, and ReflectedWaves.

Resonance of Landmine-Soil System.

Interactions of incident waves with buried landmine evident in measured data.


12

34

Wave Separation at each iteration: along top-most line array


All wave types

Only the reflected waveAfter Prony processing

Data Movies


ACTUAL TARGET : (135,135) ± 5ESTIMATE (102,120)

First Iteration


Circle constraintR=30cm Movement Penalty

Next Array Position


Values calculated on half circle

Next array position values calculated on half circle of radius=30cm


ALONE CUMULATIVE

ACTUAL TARGET : (135,135) ± 5ESTIMATE: ALONE (100,127), CUM(112,127)

Second Iteration


Values calculated on half circle

Next Array Position


ALONE CUMULATIVE


Third Iteration


ALONE CUMULATIVE


Fourth Iteration


All Four Iterations: Imaging with one array at a time


Four Iterations: Cumulative Imaging


2D array (3 X 10): Three lines having 10 sensors each

Sensors are ground contacting accelerometers

LabView is used to control the movement of array and seismic source firing and interface with MATLAB

Processing algorithms are implemented in MATLAB

Target is a VS1.6 mine buried at 5cm

Implementation


Experimental Setup of Seismic Landmine Detection Measurements

3 by 10 Element Array of Ground-

Contacting Sensors

Seismic Source

VS-1.6 AT Landmine (5cm deep)

50 Tons of Damp, Compacted Sand

Scan Region of 2m by 2m

45 cm

174 cm


Target is localized in four iterationsEach iteration takes 10 secs. for processing and 30 secs. for data acquisitionTotal time for four iterations is 2 minutesTypical raster scan takes a few hours to locate the target with a large number of measurements

EXPERIMENTAL SETUP

Demo in Sandbox


Four Iterations


Four Iterations: another run


We can further scan in line with the last estimated target positionExtract the reverse wave and try to locate the exact location of resonanceMovies shown the extracted wave with the line array of 30 sensors, which is moved toward the target with 1cm incrementTarget center is (50,50) and the starting location and position of target is shown

End-game Detection


Extracted reverse wave as array moves near and above the target

Movie: VS-1.6 (5cm)


Extracted reverse wave as array moves near and above the target

Movie: TS-50 (1cm)


Outline

Introduction






Detecting non-target areas by GPRΘ : Beamwidth of the transmitter and receiver antennas

dtr : Transmitter – Receiver Distance

h : Antenna Height

If there is no detection at this location

Shaded region does not contain targets

Instead of one step size.

Move transmitter by trdh −θtan2


Results from the GPR Air Measurements

Shotput in Air; Measured Data

10 20 30 40 50 60 70 80 90

100

200

300

400

500

600

700

800

900-200

-180

-160

-140

-120

-100

-80

-1 -0.5 0 0.5 1

x 10-9

-0.05

0

0.05

0.1

0.15

time(seconds)

Double Differentiated Gaussian Pulse

)(12FAPQ−=′ εσγ• Threshold :

• PFA is a user defined parameter

• σ2 is estimated from the data

Measured Data Theoretical Reference Signal

• Correlate with a delayed and scaled version of the transmitted signal

• Estimate the variance by taking average energy of the signals from non-target areas


Detection Test

0 200 400 600 800 1000 1200 1400 1600 1800 2000-2

0

2

4

6

8

10

12x 10

-8

Signal Correlation

Threshold

0 200 400 600 800 1000 1200 1400 1600 1800 2000-2

0

2

4

6

8

10

12x 10

-8 Detection Test at X = -72cm

Signal Correlation

Threshold

0 200 400 600 800 1000 1200 1400 1600 1800 2000-1

-0.5

0

0.5

1

1.5x 10

-7 Detection Test at X = -54cm

Signal Correlation

Threshold

No detection, movetrdh −θtan2

Detection Test for first measurement point

Target Detected !

Apply time delay difference to find the apex of the hyperbola.


Monostatic Antenna in Homogeneous Medium

tα+1 Δ tα

z0

22

221

22202

21

2201

2202

2220

)12(4

))12()((4

))1((2

))((4

)(2

Δ+=−⇒

Δ++Δ+=⇒Δ++=

Δ+=⇒Δ+=

+

++

α

ααα

αα

αα

αα

αα

ctt

zc

tzc

t

zc

tzc

t

Does not depend on target depth!

Find α which indicates how many step size the antenna is away from the target position


Target Position Found

0 100 200 300 400 500 600 700 800 900 1000-3

-2

-1

0

1

2

3

4x 10

-5 Reading at X = -54

0 100 200 300 400 500 600 700 800 900 1000-3

-2

-1

0

1

2

3

4

5x 10

-5 Reading at X = -52

TDD

tα

tα+1

α = 26 is found and the antenna is moved to target position which is X = 0


AccomplishmentsDeveloped three sensor experiment to study multimodal processing

Developed new metal detector and a radarInvestigated three burial scenariosShowed responses for all the sensors over a variety of targetsDemonstrated possible feature for multimodal/cooperative processingDeveloped new 3D quadtree strategy for GPR data

Developed seismic experiments, models, and processing Developed optimal maneuver algorithm to locate targets with a seismic array Demonstrated reverse-time focusing and corresponding enhancement of mine signatureDemonstrated imaging on numerical and experimental data from a clean and a cluttered environmentModified time-reverse imaging algorithms to include near field DOA and range estimates. The algorithms are verified for both numerical and experimental data with and without clutter.Modified wideband RELAX and CLEAN algorithms for the case of passive buried targets. The algorithms are verified for both numerical and experimental data with and without clutter. Developed a vector signal modeling algorithm based on IQML (Iterative Quadratic maximum Likelihood) to estimate the two-dimensional ω-k spectrum for multi-channel seismic data.

Developed multi-static radarDemonstrated radar operation with and without clutter objects for four scenariosInvestigated pre-stack migration imaging of multi-static data

Buried structuresDeveloped numerical model for a buried structureDemonstrated two possible configurations for a sensorMade measurement using multi-static radar


PlansThree sensor experiment (Landmine)

Incorporate reverse-time focusing and imagingIncorporate multi-static radarMore burial scenarios based on inputs from the signal processorsLook for more connections between the sensor responses that can be exploited for multimodal/cooperative imaging/inversion/detection algorithms

Imaging/inversion/detection algorithmsUse reverse-time ideas to characterize the inhomogeneity of the groundInvestigate the time reverse imaging algorithm for multi-static GPR data.Investigate the CLEAN and RELAX algorithms for target imaging from reflected data in the presence of forward waves with limited number of receivers.Develop elimination and end-game strategies for seismic detection with a maneuvering arrayInvestigate joint imaging algorithms for GPR and seismic data.

Buried Structures & TunnelsExperiments with multi-static radarDevelop joint seismic/radar experimentSignal Processing

detection of obscured targets: signal processingpeople.ee.duke.edu/~lcarin/mcclellan8.3.05.pdfgpr...

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