Detection and identification of objects basedon radio-frequency signatures

Somnath Mukherjee

Received: 15 July 2012 /Accepted: 13 May 2013 /Published online: 1 June 2013# Institut Mines-Télécom and Springer-Verlag France 2013

AbstractRadio-frequency-based intelligent proximity sensors for de-tection as well as identification of objects were presented. Aresonant structure is constructed that creates a near field withthree-dimensional extent in the same order of magnitude asthe physical size of the objects to be detected and identified.As an object is brought within the extent of the near field, aredistribution of electric and magnetic fields take place, mod-ifying the reflection coefficient (i.e., impedance) of the reso-nant structure monitored through a port. The object under test,with its own natural frequencies, perturbs the resonant fre-quency of the monitoring structure to create a unique set ofnatural frequencies (poles and zeros). These poles and zeros,depending on the size, shape, material composition, and ori-entation, constitute the RF signature of the object and can bedetermined from the measurement of reflection coefficient.This technique can be used to create smart shelves for auto-mated inventory without the need for tagging, as well for avariety of security applications. The technique can be used formetallic and non-metallic objects, as well as for a combinationthereof. The basic principle is illustrated by way of electro-magnetic simulation, and implementation of a smart tray usingthe principle is presented.

Keywords Intelligent proximity sensor . Radio-frequencyidentification . Object recognition . Object identification .

Near-field communication . Impedance spectroscopy .

Discrimination sensitivity . Smart shelf

1 Introduction

Various industries like retail, manufacturing, healthcare, etc.are motivated to automate the inventory of stocks, where

objects are organized on shelves in retail stores, warehouses,manufacturing operations, healthcare facilities, etc. There is astrong need to identify the type of object, presence/absence,and quantity (count) at an instant of time. Automated inven-tory information is very important for just-in-time replenish-ment. Another application for automated inventory is forreduction of shrinkage that can be achieved by comparinginventory on shelves with objects passing through point-of-sales terminals.

The most commonly used method for automating inven-tory is to use radio-frequency identification devices whereeach item is typically tagged using ultra-high-frequency [1]or high-frequency [2] transponders. Unless done duringmanufacturing of the item itself, individual tagging mightbe cost-prohibitive to the end user. Therefore, a method ofautomated inventory that does not require tagging of objectsis highly desirable.

Image-based systems [3] for automated inventory doaway the need for tagging of items, utilizing an array ofcameras to capture images of shelves. Video analytic soft-ware may then be used to identify individual items.However, the cameras often fail to view objects that areobstructed by other objects in proximity and the techniquehas thus limitations.

Proximity sensors have been around for decades andare used to detect the presence or absence of objects.They are broadly classified into electric and magneticvarieties, with each type specialized for non-metallic ormetallic objects. However, traditional proximity sensorsare unable to identify objects along with detection.Moreover, proximity sensors may not scale favorablyin cost.

The present work describes an intelligent proximity sen-sor (IPS) that performs identification of objects in additionto detection. The identification is done by comparing theradio-frequency (RF) signature of the detected object with aset of signatures stored in a data base. Being RF based, theimplementation is simple and cost competitive.

S. Mukherjee (*)RB Technology, Milpitas, CA 95035, USAe-mail: somnath3@sbcglobal.net

Ann. Telecommun. (2013) 68:459–466DOI 10.1007/s12243-013-0373-8

In addition to automated inventory, the IPS is a potentialcandidate for various security applications, industrial con-trol, robotics, etc.

2 Basic principle

We will first illustrate the basic technique with the help of asmall dipole, i.e., a linear wire of length l small compared tothe operating wavelength λ. For such a scenario, it is validto assume linear current distribution along the length [4].The distribution stays unaltered even when lumped circuitelements are inserted in series. As the length of the dipole issmall compared to wavelength, the radiation resistance willbe small. However, a resonance in the near field can beintroduced by the addition of suitable structures and/orlumped elements.

Figure 1 shows a typical sensing structure, where a mono-pole on a ground plane, instead of a dipole, is used to conservespace. A capacitive “hat” is added to the top and an inductivehelix inserted around the middle of the monopole. The regionabove the hat is the sensing region where the objects areintroduced for detection and identification. Complex reflec-tion coefficient (Γ in) is monitored between the monopole andthe ground plane as shown, the said Γ-frequency profilesbeing used to create RF signatures for the objects.

In the absence of the inductive component, the inputimpedance is capacitive and very little current flows throughthe monopole. The field above the hat (the sensing region) ispredominantly electric. However, introduction of the induc-tor creates a series resonance with substantial currentflowing through the monopole. Thus, in the vicinity ofseries resonance, there will be appreciable electric as wellas magnetic field in the sensing region as shown in Fig. 2a,b. The balance between electric and magnetic fields can beperturbed by the introduction of objects in the sensingregion, resulting in shift of resonant frequency. Dependingon the physical characteristics of the object, there may bemore than one resonant frequency. Moreover, if a broadbandmeasurement is carried out, it is the Γ-frequency profile thatcan be used to create the RF signature, the said profiledepending on size, shape, material composition, and orien-tation. We keep in mind that the Γ-frequency profile is an

alternative way of formulating the pole-zero behavior ofimpedance.

Introduction of the object under test creates a redistribu-tion of the electric and magnetic fields resulting in a changeof reflection coefficient. Therefore, the physical extent ofthe fields need to be such that introduction of objects createa sufficiently large redistribution of fields.

As an example, let us consider a sensor to detect andidentify objects of dimensions within 3 to 20 cm (typicalsupermarket item size).With the monopole being 1.5 cm long,hat dimensions of few centimeters and the inductance of thehelix in few tens of nanohenries, the resultant series resonantfrequency of the sensor is around few hundred megahertz. Theextent of the field strong enough to affect the reflectioncoefficient is few tens of centimeters. Γ-Frequency profilesfor common objects over an octave of frequencies or so can becreated and used for detection and identification.

It is to be noted that maximum discrimination sensitivity,i.e., the ability to differentiate most between objects, isobtained near the resonant frequency, which can be sur-mised from the following intuitive approach.

The reflection coefficient of an impedance R+ jX in animpedance environment Z0 is given by:

Γ ¼ Rþ jX−Z0Rþ jX þ Z0

ð1Þ

The phase angle of the reflection coefficient is given by:

φ ¼ −2arctanX

Z0þ R

� �ð2Þ

If the objects are assumed lossless, the sensitivity can bedefined as:

dφdX

¼ 1

1þ XZ0þR

� �2 ð3Þ

which becomes maximum for X=0, i.e., under resonancecondition.

3 Typical radio-frequency profiles

We now illustrate with examples how reflection coefficientchanges with introduction of various objects in the sensingregion. Electromagnetic simulator HFSS from Ansys Corp.was used for this purpose.

The sensing structure as in Fig. 1 was created with thefollowing parameters.

Ground plane—60×60 mm, made of copperMonopole—15 mm long, 1.5-mm diameter, made ofcopperHat—25-mm diameter made of copper

in

Hat

Helix

Ground Plane

Fig. 1 Typical sensing structure

460 Ann. Telecommun. (2013) 68:459–466

A lumped inductor 100 nH was inserted in the middleregion of the monopole. A resistor 15 K ohms wasconnected to the inductor in parallel to represent losses.

Figure 3 shows the electric (left) and magnetic (right)field for no objects in the sensing region, i.e., for the IPSalone. The sensing structure is indicated with the arrows.The magnitude of the fields, at 600 MHz, are displayed on aplane (150×256 mm) passing through the axis of symmetrycontaining the monopole. The fields are color graded be-tween 5 and 100 V/m for electric and 0.01 to 0.5 A/m for

magnetic field respectively. We notice significant electricand magnetic field at the display frequency of 600 MHz,which is somewhat higher than the series resonance of theIPS, viz. ∼550 MHz.

Next, Figs. 4a,b, and c display the fields in the sameplane when a cylindrical column of water of diameter50 mm and lengths 30, 75, and 200 mm respectively areintroduced. The reduced electric field inside the water col-umn is clearly visible; the nature of the magnetic field un-dergoes no significant changes except for 200 mm.

Fig. 2 a, b Electric and magnetic field around resonance in sensing region

Ann. Telecommun. (2013) 68:459–466 461

Figure 5 shows the polar plot of reflection coefficient(Smith Chart) over 300 to 800 MHz for the above scenarios.It is observed that for IPS alone as well as for 30- and 75-mmcolumns of water, the magnitude of reflection coefficient is

close to unity due to the minimal losses from the devicesintroduced. Most of the losses are coming from the 100-nHinductor which has a Q-factor of around 20. The above sce-narios mainly store rather than dissipate energy, but the dis-tribution of electric and magnetic fields are different in eachcase. As a result, the distinction between the profiles arisesmainly in phase. Though not clear from Fig. 5, a phase-frequency plot (not shown) reveals a significant change ofphase (∼18°) near resonance between 30- and 75-mm col-umns of water.

For the 200-mm column of water, radiation is responsiblefor a reduction in magnitude of reflection coefficient.Operation in this region, though possible in principle, needsto be handled with caution due to the restrictions imposedby regulatory agencies.

Figures 6 and 7 demonstrate the effect of introduction ofmetallic objects, the former corresponding to an aluminum

Fig. 3 Electric (left) andmagnetic (right) field IPS alone600 MHz

Fig. 4 a Electric (left) and magnetic (right) 50×30-mm water cylinder600 MHz, b electric (left) and magnetic (right) 50×75-mm watercylinder 600 MHz, c electric (left) and magnetic (right) 50×200-mmwater cylinder 600 MHz

Water

-1

-0.5

0

0.5

1

-1 -0.5 0 0.5 1

IPS

30

200

75

Fig. 5 Polar plot (Smith chart) of Γ from simulation 1

462 Ann. Telecommun. (2013) 68:459–466

cylinder of 50-mm diameter and 30-mm height, whereas thelater relates to an aluminum box of dimensions 50×50×30 mm. As expected, negligible field exists inside the me-tallic enclosures, and the profile will be solely determinedby surface geometry and material properties. In other words,it is not possible to decipher the contents inside a metalliccontainer—a task possible with dielectric containers.

Figure 8 shows the polar plot of reflection coefficient ofscenarios of Figs. 6 and 7. For comparison purpose, they areoverlaid with a water cylinder of 50-mm diameter and 30-mm height. A phase-frequency plot (not shown) reveals achange of phase of ∼5° near resonance between water andaluminum cylinders of identical dimensions (50×30 mm).The corresponding number is ∼20° near resonance for alu-minum box and cylinder.

4 Experimental results

A sensing structure was constructed with the followingparameters.Ground plane—40×40 mm, made of copperMonopole containing helix—18 mm long, made ofcopperHelix diameter—∼3 mmHelix length—∼12 mmNumber of turns in helix—6Hat—25-mm diameter, made of copperThe monopole/helix was constructed using 28AWGbus wire

A microstrip trace was present from the monopole end toan SMA connector for measurement. Reflection coefficientwas measured at the SMA connector using a vector networkanalyzer, with the microstrip trace being properly de-embedded through calibration.

Figure 9a shows polar plot of reflection coefficients ofobjects determined experimentally over 300 to 800 MHz.The green trace refers to no object, i.e., the IPS alone. Thered trace corresponds to an aluminum soft drink can ofdiameter 64 mm and height 122 mm, while the blue tracerefers to a plastic bottle 60-mm diameter×130-mm heightcontaining water-based beverage.

It is observed that the general behavior of the simulatedand experimental data is similar, but an additional reso-nance is present in the experiment. This resonance ispossibly due to parasitics in the experimental structure,especially in the helix, and its representation by a singlelossy lumped inductor is inadequate. Moreover, since theparameters of the simulated structure and the experimentalstructure are not identical, we refrain from quantitativecomparison here.

From Fig. 9a it is clear that the signatures of the bottle(dielectric) and the can (metal) are significantly differentand therefore can be distinguished easily. Figure 9b showsthat it is also possible to distinguish between objects withsomewhat similar physical characteristics. Here, the redtrace (can1) refers to an aluminum soft drink can of diameter64 mm and height 122 mm, whereas the blue trace (can2)refers to steel can of 70-mm diameter and 100-mm height.The losses (i.e., composition) in the two different objectsappear to have a negligible impact on the magnitude of thereflection coefficient. However, the small change in dimen-sions creates a significant shift in balance of electric andmagnetic energies stored, manifesting as substantial change(∼50° near resonance) in phase-frequency characteristics.

Fig. 6 Electric (left) and magnetic (right) 50×30 mm Al cylinder600 MHz

Fig. 7 Electric (left) and magnetic (right) 50×50×30 mm Al box600 MHz

-1

-0.5

0

0.5

1

-1 -0.5 0 0.5 1

water cyl

Al cyl

Al box

Fig. 8 Polar plot (Smith chart) of Γ from simulation 2

Ann. Telecommun. (2013) 68:459–466 463

5 Design considerations

If the hat is circular and symmetric about the axis of themonopole, then the current at the tip of the monopole can beassumed zero because of radial currents flowing in all direc-tions (Fig. 10). Further, if the length of the monopole, is small

compared to the wavelength, the current distribution can beassumed linear [4] as shown in Fig. 10. Introduction of alumped component in series is not expected to change thecurrent distribution appreciably as by definition the lumpedcomponent is small compared to wavelength. Consequently,the fields generated by the sensing structure can be determinedfrom the corresponding expressions for a monopole [5].

Increasing the length of the monopole increases the fields,but space constraints often limits the length. Also, lengthcannot increase beyond a point such that the upper limit ofunintentional radiation set by regulatory agencies is violated.

If the application requires close range operation (e.g.,inventory on a shelf), near field approximations (2π /1.distance≪1) may be used. For excitation current stayingthe same, lowering the frequency will increase the electricfield (Er and Eθ) and therefore sensitivity.

For security applications, there is a requirement for some-what extended range, and intermediate field approximation(2π /1 distance>1) may be used. Here, increasing the frequen-cy increases the magnetic field (Hϕ) and electric field (Eθ).

Introduction of large-enough objects (metal or dielectric)encourages radiation, as these objects in conjunction with theIPS, constitute a radiating structure. Therefore, while estimatingunintentional radiation, representative objects of largest possi-ble size need to be considered. Since coherent detection requir-ing very narrow bandwidth is used to measure reflection coef-ficient, fortunately RF power can be restricted to low values.

Measurement of phase is very important as a large cate-gory of objects present minimal losses and therefore hasinsufficient impact on the magnitude of reflection coeffi-cient. This category of objects are however identified by theappreciable phase change was produced due to redistribu-tion of stored electric and magnetic energies. Fortunately,accurate phase measurement to a degree or so can beperformed economically.

It is obvious that the contents inside a dielectric containercontribute to the signature, and therefore could be used fornon-invasive measurement of contents. However, for metalcontainers the inside contents do not play a role in generat-ing a profile.

6 Prototype smart tray

A photograph of the prototype “Smart Tray” is shown inFig. 11. The prototype tray consisted of ten IPSs arranged ina grid fashion—with each IPS selectable through a RFswitching matrix. The reflection coefficient can be deter-mined by a scheme as in Fig. 12, where C1 and C2 aredirectional couplers that separate the transmitted andreflected waves and are compared in a magnitude-phasecomparator (e.g., AD8302 from Analog Devices, Inc.) toobtain complex reflection coefficient. The device could

I

Distance

HatHat – top view

Side view

Fig. 10 Current distribution in monopole

-1.0

-0.5

0.0

0.5

1.0

-1.0 -0.5 0.0 0.5 1.0

-1.0

-0.5

0.0

0.5

1.0

b

a

-1.0 -0.5 0.0 0.5 1.0

Can1Can2

IPS

Can

Bottle

Fig. 9 a Polar plot (Smith chart) of Γ from experiment 1, b polar plot(Smith chart) of Γ from experiment 2

464 Ann. Telecommun. (2013) 68:459–466

provide an effective phase resolution of less than 2° at theoperating frequencies.

Each IPS can be trained to identify N number of objectsbased on same number of Γ-frequency profiles stored in acomputer. Each profile contains M number of frequencypoints, and therefore the ith profile can be defined as:

Γ i ¼ Γ i; j

� �j¼1;M i¼1;N ð4Þ

The profile of the test object is similarly described as

Γ test ¼ Γ test j� �

j¼1;M ð5Þ

A figure of merit Ei can be defined as

Ei ¼XMj¼1

à test j�� ��2

Γ test j−Γ i; j

�� ��2 ð6Þ

If Ei>T, T being a pre-defined positive number(threshold), it can be inferred that a match has occurredbetween the object under test with the ith profile. It is tobe noted that the parameter Ei is analogous to signal to noiseratio and can often be treated as such.

As an object is moved away from its reference location(i.e. the location at which the Smart Tray has been trained),Ei decreases, but it is still possible to identify objects up to acertain wander distance if T is not set too high. This makesthe system robust in performance under practical situations.

7 Future work

A rigorous theoretical framework needs to be established forvalidating the hypothesis that a particular object with uniquegeometry, material composition, and dimensions create aunique Γ-frequency profile at a measurement port. Forsimplicity, the initial treatment can be based on quasi-static(non-radiating) assumptions, but eventually should includeradiating fields since the effect of permissible radiation ondiscrimination sensitivity need to be determined.

A more accurate circuit model for the sensing structureneeds to be established for explaining the parasitic reso-nances discussed in Section 4.

The sensing structure discussed herewith, though of mod-erate height (∼18 mm), is three-dimensional in nature. Certainapplications, e.g., volumetric sensing for security and auto-mated inventory, require a two-dimensional sensing structurefor space conservation and convenience. A two-dimensionalstructure can be fabricated using very low cost printing oretching techniques. A logical start for the two-dimensionalstructure could be the dual of the resonant monopole, e.g., aresonant loop.

To correlate the signature of a measured profile with theones in the database, a straightforward mean-square-error ap-proach has been used. More sophisticated algorithms to distin-guish between objects that have similar electromagnetic be-havior (and therefore signatures), could be investigated. Suchalgorithms, such as matched filter detection, might provideincreased discrimination sensitivity and robustness against“noise” introduced due to change in position and orientation.

Fig. 11 Prototype smart tray

Radio Frequency Synthesizer

Magnitude Phase

C1 C2

Magnitude and Phase Comparison

IPS

Object to be

Identified

RF Switch Matrix

Fig. 12 Measurement of reflection coefficient

Fixed Grid Versatile Grid

Fig. 13 Grid structure—fixed and universal

Ann. Telecommun. (2013) 68:459–466 465

Artificial intelligence principles may be used to perpetuallytrain the sensor for improved performance.

As we demonstrated in the Section 3, a container made ofdielectric material will generate unique signatures for variouslevels of liquid (or granular solid) it contains. Therefore, it ispossible to use this technique for non-invasive measurement ofcontents such as liquids or granular solids [6]. A further advan-tage of this method is absence of any guiding structure as in [7].

The smart tray (Section 6) has a grid structure (Fig. 13) witheach “square” earmarked for items of comparable footprint.Items too large compared to the grid size cannot be accommo-dated as shown in top right of “Fixed Grid” in Fig. 13. On theother hand, if the item is too small compared to grid dimen-sions (bottom right in “Fixed Grid” in Fig. 13), the figure ofmerit Ei (Eq. 6) introduced due to position wander could be toolow. To overcome this, the smart tray can include grids ofvarious sizes, but that restricts its operational flexibility andversatility. To construct a truly versatile smart tray capable ofidentifying objects with random footprints, the grid can bemade be small compared to the item footprints. By processingadjacent non-empty grids, an item with random footprint canbe identified. Furthermore, the position of the item is deter-mined automatically. However, the complexity of the RFswitch matrix is increased several times.

8 Conclusion

An intelligent proximity sensor for detection and identificationof objects is presented. Being electromagnetic in nature (i.e.,utilizing both electric and magnetic fields), it has the ability todetect and identify objects that are metallic, non-metallic, or acombination thereof. This is in contrast to capacitive (electricfield based) proximity sensors suitable for dielectric materials,and magnetic (eddy current based) proximity sensors forconducting materials. The intelligent sensor derives its identi-fication property from operation around the resonance region,

in contrast to traditional proximity sensors operating near DC.The intelligent sensor operates on the fact that a certain objectuniquely perturbs the balance of stored electric and magneticenergies, creating unique frequency-reflection coefficient pro-files at a measurement port. The frequency-reflection coeffi-cient profiles constitute signatures that can be used for identi-fication. As the sensor does not necessarily depend on thepower absorption property of the objects, it can detect andidentify lossless objects based on change of phase of reflectioncoefficient occurring due to redistribution in stored electric andmagnetic energies. The technique is amenable to low costimplementation and has the potential for multiple applications.

Acknowledgment The author thanks Sanschip Inc. of San Jose, CA,USA for supporting the work.

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