detection of stationary foliage-obscured targets by polarimetric millimeter-wave radar

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 43, NO. 1, JANUARY 2005 13

Detection of Stationary Foliage-Obscured Targets byPolarimetric Millimeter-Wave RadarAdib Y. Nashashibi, Senior Member, IEEE, and Fawwaz T. Ulaby, Fellow, IEEE

AbstractThis paper introduces a technique to detect thepresence of foliage-obscured targets using millimeter-wave radarsoperating at near-grazing incidence. Radar return from a scenewhere a target is concealed behind foliage consists of both foliagebackscatter return and a polarimetrically distorted and reducedtarget return. A data acquisition and signal processing techniquethat takes advantage of temporal decorrelations in foliage isproposed to enhance target detection and resolve it from foliage.Furthermore, an inversion technique is proposed to remove thepolarimetric distortions in the target response. The techniques areverified through a series of indoor and outdoor experiments.

Index TermsMillimeter-wave radar, propagation, target de-tection.

I. INTRODUCTION

DETECTIONoftargetsobscuredbyfoliagehasbeenthesub-ject of intense research for a number of years. The relativepositions of foliage and targets in a given scene can, in general,be used to assign the scene to one of two main scenarios: 1) thetarget is hidden under foliage, deep inside a tree stand or 2) thetarget is positioned behind a line of trees or a large bush in anotherwise open terrain. Considerable experimental and theoret-ical research has been carried out to examine the feasibility of de-tecting targets positioned deep inside a tree stand using imagingradars operating at very high frequency through ultrahigh fre-quency bands [1][5], primarily in a side-looking radar configu-ration. Although electromagnetic waves at these low frequenciescanpenetrate throughfoliagewith lessattenuation than thehighermicrowave and millimeter-wave (MMW) frequencies, difficul-ties in achieving the desired false-alarm rate and probability ofdetection have hindered the development of operational systemsthus far. These difficulties arise, in part, from the fact that falsealarms generated by the trunkground interactions may producebackscatter levels comparable to those due to reflections fromhardtargets.Recently,MMWnadir-lookingradarshavebeenpro-posed for detecting targets positioned under tree canopies [11],[12]. The premise for the use of MMW radars is: 1) signals atMMW frequencies can penetrate through few layers of foliagewithsomefiniteattenuation;2) thereareaconsiderablenumberof

Manuscript received February 27, 2004; revised September 15, 2004. Thiswork was supported in part by the Advanced Sensors Consortium through theU.S. Army Research Laboratory under the Federated Laboratory Programs Co-operative Agreement DAAL01-96-2-0001, and in part by the Advanced SensorsConsortium through the U.S. Army Research Laboratory under the Collabora-tive Technology Alliance Programs Cooperative Agreement DAAD19-01-2-0008.

The authors are with the Radiation Laboratory, Department of Electrical En-gineering and Computer Science, the University of Michigan, Ann Arbor, MI48109-2122 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/TGRS.2004.838378

Fig. 1. Application of land vehicle-mounted MMW radars to detect hardtargets positioned behind bushes and/or line of trees.

openings throughmost foliagecovers; and3)veryhighresolutionand far more compact and lightweight radar systems can be de-signed at MMW than at lower frequencies. Based on single-looknadir-looking measurements of a tree stand with vehicles posi-tioned underneath, a target detection technique, specific to theaforementioned radartarget configuration, was developed [12].In this technique, the absence of ground reflections, due to thetarget shadow, over a substantial ground area is used to reduce thelargeimagedareatoasmallsetofsuspectedareas.Then, thestatis-ticsofthemeasuredradarreturnfromrange-binsabovethegroundare used to determine the presence of a target in each suspectedarea. The success of nadir-looking MMW radars in detecting tar-gets under tree canopies is due, in part, to the fact that the radarsignal propagates through foliage over a limited distance, namelythe height of the canopy. As suggested in [12], the combinationof low-frequency ultrawideband side-looking imaging radar andnadir-lookingMMWwidebandimagingradarcanbecomeapow-erful tool for detecting targets hidden inside a tree stand.

This paper is concerned with the second scenario, namely, thecase of a target positioned behind a line of trees or large busheswhile being illuminated by a side-looking radar, as depicted inFig. 1. Side-looking MMW radars, operating at near-grazing in-cidence and mounted on ground-based or low-flying vehicles,are already in use in several civilian and military applications.Doppler shifts in the radar return in these systems can be usedfor detecting moving targets. However, if the target is stationaryand hidden behind foliage, then Doppler cannot be used as thesole detection feature, since it might falsely identify the targetas part of clutter, especially if proper signal processing and de-tection algorithms were not implemented.

At MMW frequencies, both the high permittivity and largeelectrical dimensions of the foliage constituents (severalwavelengths in size) result in: 1) significant radar return inthe backscatter direction and 2) substantial attenuation andpolarimetric distortion of the propagating wave. For example,in a recent study conducted at The University of Michigan, itwas found that the extinction rate varied between 213 dB/m

0196-2892/$20.00 2005 IEEE

14 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 43, NO. 1, JANUARY 2005

(a) (b)Fig. 2. Presence of foliage in front of an object distorts the targets radar signature at MMW frequencies. (a) No foliage. (b) Behind foliage.

for different tree canopies measured at grazing incidence [10].These results are consistent with another set of foliage atten-uation measurements performed using nadir-looking MMWradars [8]. In effect, an MMW signal propagating for hundredsof meters in a forest stand at near grazing incidence will sufferexcessive attenuation and distortion, prohibiting the detectionof objects hidden deep in the tree stand. However, signal atten-uation and distortion can be tolerable in cases where the objectis hidden behind only a few meters of foliage.

Consider the wideband (high range resolution) MMW radarilluminating the target scene depicted in Fig. 1. Examples of themeasured radar return are shown in Fig. 2(a) for an unobscuredvehicle and Fig. 2(b) for a vehicle obscured by vegetation. A re-duced and distorted radar cross section (RCS) of an object mightresult in false detection and/or identification by standard auto-matic target recognition (ATR) algorithms. Hence, to improvethe probability of detection of a hidden target, proper data ac-quisition and signal processing techniques need to be developedin order to resolve the target return from the foliage return first,and then, minimize (or remove) the distortions incurred uponthe targets radar signature.

The approach proposed in this paper for detecting targets be-hind bushes is based on the temporal decorrelation phenomenonof radar return from foliage. While the target can choose to re-main stationary, or if it moves, all its scattering centers wouldmove with the same velocity, the positions and orientations ofleaves and branches comprising a bush or tree canopy will tendto vary quasi randomly (under the influence of wind). Motionon the order of a fraction of a wavelength is enough to producea different radar return from foliage. For example, a leaf dis-placement on the order of 1 mm is sufficient to introduce sub-stantial round-trip phase-angle variations at 35 GHz (8.6 mm inwavelength). Hence, the radar return from any given range-binwithin the foliage (which is the coherent sum of the backscat-tered radar signals from all leaves within that range-bin) willdecorrelate rapidly with time. As will be discussed later, decor-relation time due to moderate wind speeds (2532 km/h) is onthe order of 1015 m/s at 35 GHz. If repeated radar scans ofa scene are permitted, then temporal decorrelations in the fo-liage return can be exploited in two ways: 1) to determine thepresence of a stationary object (like a parked vehicle) behind arandom (dynamic) target (foliage under any natural wind con-dition) and 2) characterize the distortions induced by foliageon the object and restore its signature [9]. For a realaperture

35-GHz radar system of 500-MHz bandwidth equipped with aone-dimensional horizontally aligned 1-m-long antenna array(whose beamwidth in azimuth is 0.6 ) used to image a targetscene 200 m away, the resulting image pixel size will be on theorder of 2.0 m in azimuth and 0.3 m in range. If instead a 95-GHzradar is used, then the resulting pixel size is on the order of 0.7 min azimuth and 0.3 m in range, which is sufficient for imaginga large target such as a truck or a tank. Such a system can beeasily mounted on a ground vehicle.

This paper introduces, and validates experimentally, a dataacquisition and processing technique to detect the presenceof foliage-obscured targets using MMW radars operating atgrazing incidence. This technique exploits temporal decor-relations of radar return from foliage under the influence ofwind to detect a stationary target. The paper also proposes aninversion technique to remove polarimetric distortions in thetargets response. In the next section, wind-induced temporaldecorrelations of the radar return from foliage are discussed. InSection III, a data acquisition and processing technique aimedat resolving the obscured target from foliage is presented andexperimentally verified. Finally, a postprocessing techniquethat enhances target detection by removing distortions incurredby the foliage on the target signature is presented.

II. WIND-INDUCED TEMPORAL DECORRELATIONOF FOLIAGE COVER

Foliage can be modeled as a collection of randomly orientedand positioned scatterers occupying a given volume. The totalbackscattered electric field from these scatterers is the co-herent sum of the fields scattered from all scatterers in thatvolume

(1)

where is the incident field, is the field scattered fromthe th scatterer, is the wavenumber ( , and isthe wavelength), and and are the complex scattering am-plitude and position of the th scatterer, respectively. A windblowing on foliage will force its constituents to change theirorientations and relative positions in order to reach a new equi-librium under the influence of various forces acting on them,

NASHASHIBI AND ULABY: DETECTION OF STATIONARY FOLIAGE-OBSCURED TARGETS 15

including wind and gravity. These changes in scatterers orien-tations and relative positions are responsible for the temporaldecorrelation in the radar return

(2)

where is the time lapse being considered between field mea-surements. Reorientation and repositioning of the th scatterer,for example, will lead to changes in its scattering amplitudeand its round-trip phase propagation factor and will re-sult in a different . A 1-mm change in the distance betweena given scatterer (e.g., a leaf) and the radar results in an 84change in its round-trip phase angle at 35 GHz. Since most fo-liage constituents are electrically large at MMW frequencies,their backscattered responses are strong functions of orienta-tion. Fig. 3(a) depicts the RCS of two different leaves as a func-tion of orientation angle in azimuth . The physical areas of thetwo square leaves were 2.5 2.5 cm and 5.0 5.0 cm . In thisfigure, the resistive sheet model [13] was used to compute themonostatic radar cross section of the two leaves at 35 GHz. Bothleaves had identical gravimetric water content andorientation. At normal incidence , maximum radar re-turn is expected for both leaves as shown in Fig. 3(a). The sensi-tivity of radar return from a leaf to slight changes in its orienta-tion, relative to the direction of propagation of the incident field,can be best demonstrated using the normalized angular correla-tion function (ACF). The backscattered electric field from a leaf,whose RCS pattern is shown in Fig. 3(a), was used in the dis-crete form expression of ACF

(3)

where is the angular shift being considered, is thenumber of discrete orientation angles in azimuth used to gen-erate the RCS pattern, and is the electric field computed atthe th orientation angle in azimuth. The normalized angularcorrelation functions for the two leaves are plotted in Fig. 3(b) asa function of incremental changes in azimuth orientation angle

. Small changes in the azimuth orientation angle, on theorder of 3.5 and 7 for the large and small leaves, respectively,are sufficient to decorrelate the radar return.

It should be noted that, in general, many physical param-eters of foliage have a direct impact on how foliage fluttersunder the influence of a prevailing wind. Among these param-eters are wind speed and direction, geometry of canopy, totalleaf area, architecture of secondary branches and distribution ofleaves on them, relative orientations of leaves with respect towind direction, elasticity of stems connected to leaves, densityof foliage constituents, and distribution of gaps in the canopy.A thorough characterization and modeling of temporal decor-relation of radar return from wind-influenced foliage in termsof the aforementioned parameters is rather complex and beyondthe scope of this paper. Nevertheless, there are three importantquestions that need to be addressed, especially if the phenom-enon of temporal decorrelation of the radar return is to be usedin target detection.

(a)

(b)Fig. 3. (a) Numerically simulated monostatic RCS of two different leavesat 35 GHz, as a function of orientation in azimuth and (b) the correspondingnormalized angular correlation function of the two leaf sizes.

Can temporal decorrelation be observed and quantitativelycharacterized at MMW frequencies?

What is the range of values typical for the decorrelationtime at these frequencies?

How is the polarimetric RCS of a target positioned behindfoliage altered by the foliage decorrelation?

The remainder of this section will be devoted to answering thesequestions.

An earlier study, conducted by Narayanan et al. [6], investi-gated temporal decorrelations in wind-influenced trees using amonostatic X-band radar. As expected, it was observed that thebackscattered electric field decorrelated faster at higher windspeeds. It was also found that the decorrelation time de-fined as the time lapse necessary for the temporal correlationfunction of the backscattered field to drop to from its max-imum value, depended on tree type, structure, and leaf-covercharacteristics.Measureddecorrelationtimesformoderatewindsbetween 79 m/s (2532 km/h) were between 4060 m/s over


Fig. 4. Indoor experimental setup to characterize the statistics of radar returnfrom both a small tree under the influence of wind and a point target positionedbehind the tree.

several types of trees with one case as low as 14 ms. Even atlightwindspeeds,between14m/s, temporaldecorrelationcouldbe observed, albeit at longer decorrelation times. Although nosuch measurements were performed at MMW frequencies, theresults reported in [6] can be readily extended to MMW fre-quencies. At 35 and 95 GHz (whose wavelengths are 8.57 and3.16 mm, respectively), slight changes in leaf position results inphase errors that are four and ten times higher, respectively, whencompared to that induced at X-band. In addition, dimensionsof leaves and other foliage constituents will be electrically fourand ten times larger at 35 and 95 GHz, respectively, compared toX-band. Hence, the decorrelation time in moderate wind speedscan be estimated, to first order, as 1015 m/s at 35 GHz and46 m/s at 95 GHz. These relatively short decorrelation timespermit several scans of the foliage-obscured target area withina short period of time (on the order of 12 s).

A. Indoor Experimental ValidationTo demonstrate that the radar return from wind-influenced

foliage decorrelates over time at MMW frequencies, a seriesof indoor experiments was performed. In these experiments, afully polarimetric wideband monostatic radar [7] operating at35 GHz was used to measure the radar return from a stationarypoint target positioned behind a small Ficus tree. The radarwas operated over a bandwidth of 500 MHz (which provided0.3-m range resolution). A 2.75-in trihedral, with theoreticalRCS dBsm, was used as a point target and positioned10.35 m away from the radar. A 1.0-m-diameter Ficus tree waspositioned between the radar and the trihedral at 9 m away fromthe radar, as depicted in Fig. 4. Since the transmit/receive an-tenna assembly of the polarimetric radar had a beamwidth of 2 ,the diameter of the effective radar spot, computed at the center ofthe tree, was 0.31 m, which is well within the tree boundary. Inaddition, an electric fan, positioned near the radar and facing thetreetrihedral setup, was used to blow air in the direction of thetreetrihedral setup. The radar system measured the scatteringmatrix of the treetrihedral setup over the 500-MHz bandwidthwithin 3.2 s. This short data acquisition time ensured that co-herence between the scattering matrix elements was maintained.Polarimetric measurements of the wind-influenced treetrihe-dral setup were repeated every 2 s, and over 100 temporal sam-ples were collected while the foliage was fluttering. The longtime lapse between the consecutive measured samples guar-anteed that radar returns from foliage were statistically inde-pendent. Through proper software gating [7], the polarimetricradar returns from the foliage and the trihedral were isolated

Fig. 5. Normalized temporal decorrelation function for both the foliage(Ficus tree) and foliage-obscured trihedral as a function of time lapse. Thevv-polarized element of the scattering matrix was used in computing thedecorrelation function.

and stored. Then, the scattering matrices of the different targets(foliage and trihedral) were derived from the measured scatteredfields using

(4)

where is the 2 2 measured complex scattering matrix de-duced from a given range-gated radar return, is the distancecorresponding to the center of the range-bin of interest, and isthe incident electric field. The scattering matrix of the target(s)within a given range-gate can be related to through:

. In deriving from the measured scattered field, onlysystematic errors were removed via a proper calibration proce-dure [7]. The round-trip phase propagation factor , whichis common to all elements of the scattering matrix, was not re-moved. This was due in part to the difficulty in measuring thedistance from the radar to the target to within a fraction of thewavelength ( mm).

It should be noted that during the course of measurements, thetrihedral was measured with and without the foliage in front ofit. The measured radar return from the trihedral in free space (nofoliage) had in excess of 60 dB in SNR. Several datasets wereobtained by: 1) repeating the measurements with the tree rotatedin azimuth at 90 steps (100 temporal samples were collectedat each step) and 2) measuring other Ficus trees. Overall, themean two-way attenuation through foliage, as deduced from themeasured RCS of the trihedral, varied between 530 dB. Whileseveral datasets were collected of the treetrihedral setup, onlyone dataset will be presented here in detail. Nevertheless, allconclusions drawn from this set apply to other datasets as well.For the dataset under consideration, the mean two-way attenua-tion through foliage was about 10 dB. In addition, the trihedralsmean RCS was 6.5 dB higher than the mean RCS of foliage po-sitioned in front of it. The normalized temporal decorrelationfunctions for both the foliage and the trihedral, computed usingthe vv-polarized element of the measured scattering matrix, areplotted in Fig. 5 as a function of time lapse index. In this config-uration, the vv-polarized radar return from the foliage-obscured


Fig. 6. Real and imaginary components of the measured complex vv-polarized element of the scattering matrix for (a) foliage and (b) foliage-obscured trihedralare plotted for all 100 temporal samples.

trihedral maintained maximum correlation between all temporalsamples (i.e., maintained signal coherence) while it took a singletime lapse (2 s) for the radar return from the wind-influenced fo-liage to become completely uncorrelated. Because the measure-ments were repeated every 2 s, the results of these experiments,while they confirm that the decorrelation time is shorter than 2 sat 35 GHz, even under light wind conditions, they do not specifyas to how much shorter than 2 s really is.

For a more explicit presentation of the measured data, thevv-polarized elements of the scattering matrix, for both thefoliage and the foliage-obscured trihedrals, are plotted inFig. 6(a) and (b), respectively. In these two figures, all 100temporal samples are plotted in the realimaginary plane (dia-mond-shaped symbols) along with the mean coherent response(solid line), which was computed by averaging coherently the100 data points. As expected, fading in the measured radarreturn from wind-influenced foliage is responsible for therandom distribution of the foliage data in the realimaginaryplane and for the small mean coherent response. However,the radar returns from the stationary target (trihedral) exhibita much narrower distribution in the realimaginary plane, asshown in Fig. 6(b), and the computed mean coherent responseis approximately equal to the individual data points. The smallfluctuations in the trihedral response can be attributed to: 1)temporal fluctuations in the two-way attenuation rate throughfoliage and 2) fluctuations in the incoherent, forward-scatteredfields from foliage constituents that are reflected back to theradar via the trihedral. It can be concluded from Fig. 6(b)that signal attenuation and temporal decorrelation due to thepresence of wind-influenced foliage do not affect the coherenceof the stationary target return as along as a substantial portionof the field incident on the target is coherent (i.e., the diffuse orincoherent scattered field from foliage is small compared to theincidentyet reducedcoherent field).

Many theoretical and experimental studies have shown thatthe real and imaginary parts of the complex backscattered

field from statistically homogeneous clutter can be charac-terized as independent zero-mean Gaussian random variables(hence ) [14], [15]. The mean coherent responsefrom foliage in Fig. 6(a), albeit small, is not zero. This canbe attributed in part to the small number of temporal samplesused in computing the mean. Another explanation is that underweak wind conditions, such as the case at hand, not all foliageconstituents will generate a temporally varying radar return.A large branch, for example, can remain stationary under theinfluence of a weak wind [6], and its radar return can remaintemporally coherent, while simultaneously, the collective radarreturn from all foliage constituents within the same range-bincan decorrelate over time. The nonzero mean value ofdepends on the relative strengths of the stationary and thetemporally varying constituents of foliage. The presence ofstationary, albeit weakly scattering, foliage constituents impactalso the statistics of the received power. Had measurementsbeen performed over statistically independent spatial samples,then the measured power would have followed the exponentialprobability density function (pdf). Note that the ratio of themean to standard deviation is 1 in the case of an exponentialpdf. The probability density functions (derived from histogramsof the measured data) of the measured power for both foliage(Ficus tree) and foliage-obscured trihedral are plotted in Fig. 7.The mean measured power for foliage was used to generatethe exponential pdf, shown as a dotted line in Fig. 7. Despitethe apparent conformity between the exponential pdf and themeasured pdf for foliage, the ratio between the mean andstandard deviation of the measured pdf was 1.3, due to thepresence of stationary, weakly scattering foliage constituents.This uncertainty in the pdf of wind-influenced foliage, in agiven range-bin, complicates usage of detection techniques thatare based solely on received power and its statistics. In the nextsection, an alternative technique for resolving the target fromfoliage that is based on detecting changes in the radar returndue to wind-induced temporal decorrelation is discussed.


Fig. 7. PDFs of the measured vv-polarized backscatter power from both thefoliage (Ficus tree) and foliage-obscured trihedral.

III. TECHNIQUE TO RESOLVE TARGET FROM FOLIAGEAs mentioned before, a target positioned behind foliage will

have its radar return reduced due to attenuation and its polari-metric signature distorted due to the presence of electricallystrong scatterers (foliage constituents) in the radar signal path.Through repeated scans of the wind-influenced foliagetargetscene using a coherent, polarimetric high-resolution MMWradar, temporal decorrelation in foliage return can be used tofirst detect the presence of the stationary target and then resolveits position behind the foliage. The averaging process of allscans should be performed over all range-bins comprising theradar scene of interest and need to be done on some compo-nent or components of the measured radar return, such as thebackscattered power or field. In this paper, we shall refer tothese components as detection features. In what follows, twodetection features are considered and the technique using bothfeatures is tested both indoors and outdoors.

A. Detection FeaturesTwo detection features were identified, the power of the mean

backscattered field and the normalized coherence parameter. The power of the mean backscattered field can be expressed

in terms of the scattering matrix elements, v or h ,using the following expression:

(5)while the normalized coherence parameter, which is a measureof the correlation between the co- and cross-polarized returns,can be expressed as

(6)

where denotes here the ensemble average over time.The power of the mean scattered field is a relatively

simple detection feature that requires the use of a simpler radarsystem that collects data using a single transmit/receive polar-ization combination, preferably the copolarization configura-

Fig. 8. Real and imaginary components of the complex coherence parameter(S S ) computed at each of the 100 temporal samples and for both thefoliage-only case and the foliage-obscured trihedral case.

tion (i.e., vertical or horizontal polarization). Its performanceis straightforward to characterize and lends itself directly to theresults in Figs. 5 and 6. However, it is limited by: 1) the dif-ficulty in identifying a clear threshold reference, since foliageattenuation rate can be different under different foliage-coverconditions, and therefore, the RCS of the target might be weakto start with and susceptible indirectly to foliage fluttering and2) the requirement that the radar maintain its exact position rel-ative to the target during the repeated scans to within a fractionof a wavelength. The latter requirement is due in part to the diffi-culty in removing the round-trip phase propagation factorfrom the measured response.

The normalized coherence parameter, on the other hand, isimmune to the limitations affecting as a detection feature.Phase conjugation of one of the scattering elements in (6) re-moves the sensitivity of to the round-trip phase propagationfactor (which is common to all elements of the scattering ma-trix). Hence, the radar system can be moving toward the targetor away from it while repeated scans are being performed and

is being used as a detection feature, provided that the targetremains in the same range-bin or the proper range-bins fromconsecutive scans are averaged together. In addition, for the firstradar scan, the magnitude of is equal to 1 for all range-bins.Hence, the reduction in the magnitude of from 1, after av-eraging over several scans, can be used to signal the presence oftemporally decorrelating scatterers (foliage).

It has been shown that for homogeneous clutter, the mag-nitude of the normalized coherence parameter reduces to zerowhen cross correlation is performed between copolarized andcross-polarized elements of the scattering matrix (e.g.,

when spatial averaging is performed over large enough sam-ples) [15]. This observation is expected to hold true in the case ofaveraging over temporally decorrelated radar returns, especiallyfrom homogeneous foliage. However, the zero value forwill not be realized if some foliage constituents, such as largebranches, remained stationary during measurements. In Fig. 8,


Fig. 9. Indoors experimental setup whereby three dihedrals were positionedbehind three Ficus trees. The dihedrals were 0.5 m apart in azimuth. The twoouter dihedrals were about 1 m behind the trees, and the center dihedral waspositioned 2 m away from the trees.

the cross correlation between and data, namely ,is plotted in the realimaginary plane for all 100 temporal sam-ples that were collected for both the foliage (Ficus tree) andfoliage-obscured trihedral. Despite the wide spread in foliagedata, it does not uniformly fill the realimaginary plane and itsexpected value is small, but not zero. In contrast, the trihedraldata is confined to a relatively small area in the realimaginaryplane, as can be seen in Fig. 8. Note that the trihedral is a non-depolarizing target. Hence, the value of of the trihedralin free space is expected to be very small, if not zero. When thetrihedral is positioned behind foliage, as shown in Fig. 4, anycross-polarized return is due to forward-scattered cross-polar-ized fields from foliage that are reflected by the trihedral towardthe radar. Hence, the cross-polarized return from the range-bincontaining the trihedral is actually generated by the foliage. Thisexplains the spread in of the trihedral and its low magni-tude compared to alone [see Fig. 6(b)]. In fact, whenis used as a detection feature, trihedral-type targets (i.e., nonde-polarizing targets) represent the worst case scenario. In case ofa more complex target, such as a vehicle, it is expected that thetarget itself will depolarize the radar signal, and its willnot be sensitive to foliage, as will be shown in the followingexamples.

B. Imaging of Foliage-Obscured Point Targets: Indoor CaseThe 35-GHz polarimetric radar system described previously

was used to image the experimental setup depicted in Fig. 9.In this setup, three small dihedrals (whose theoretical RCS wason the order of dBsm) were positioned behind three smallficus trees. Photographic pictures of the experimental setup areshown in Fig. 10. The dihedrals were 0.5 m apart in azimuth,and the two outer dihedrals were 1 m behind the trees, whilethe center dihedral was positioned 2 m away from the trees. Asbefore, an electric fan was positioned near the radar and wasused to blow low-velocity wind toward the treedihedral setup.The gimbal-mounted radar scanned repeatedly in azimuth andmeasured the polarimetric radar response of the setup. The radarreturn from the middle dihedral was 7 dB below the maximumreturn from foliage, while the radar returns from the other twodihedrals were comparable to the return from foliage in front ofthem. Nevertheless, all returns, from foliage and from dihedrals,

(a)

(b)Fig. 10. Photographs depicting the indoors experimental setup used. (a) Viewfrom the radar perspective of the Ficus trees with the dihedrals positioned behindthem and (b) the radar viewed from just behind the foliage.

enjoyed at least 30 dB in SNR. Two radar images of the targetscene, the first based on a single scan and the second based onthe coherently averaged vv-polarized scattering matrix elementover 25 scans, are shown in Fig. 11. As can be seen from thefigure, coherent averaging of the temporally decorrelated radarreturn from foliage has led to a reduction in foliage averagereturn without affecting the return from the obscured dihedrals.The RCS of the target-to-clutter (vegetation) ratio for all threedihedrals is summarized in tabular form in Fig. 11 for both asingle scan and 25 averaged scans. In computing for any givendihedral, the return from foliage directly in front of the dihedralwas used. Coherent averaging of temporally decorrelated radarreturns from foliage has led to about 10-dB improvement in thetarget-to-clutter ratio for dihedrals 1 and 3.

The polarimetric data were also used to generate two imagesof the same target scene, using in this case the normalized co-herence parameter . In Fig. 12, the first image presents themagnitude of in decibels that was generated from a singleradar scan, while the second image presents the magnitude of themean (averaged over 25 temporally decorrelated scans).As can be seen from the figure, the normalized coherence pa-rameter of temporally decorrelated foliage exhibits a reductionin magnitude in comparison to the obscured dihedral when mul-tiple scans of the tree-target scene are used. The observed reduc-tion in indicates the presence of strong stationary targetsin an otherwise temporally decorrelating environment. The ab-solute value of , averaged over 25 scans, is summarized intabular form in Fig. 12 for the three dihedrals and the foliagedirectly in front each of them.


Fig. 11. Comparison between a single-scan radar image and the radar imagegenerated by coherently averaging the radar return of 25 scans. In the table, Tis the RCS target-to-clutter (vegetation) ratio.

Fig. 12. Comparison between the magnitudes (in decibels) of the normalizedcoherence parameter generated from a single radar scan and by averaging 25scans. Absolute values of , averaged over 25 scans, for both the dihedralsand the foliage directly in front them are summarized in the table.

C. Imaging of Foliage-Obscured Stationary Target:Outdoor Case

The same radar system was used outdoors to image, in az-imuth, a vehicle positioned behind a line of white pine trees

as depicted in Fig. 13. Similar to the indoor measurements, thegimbal-mounted radar system performed repeated scans of thefoliage-vehicle scene, except that no fan was used to generatewind effects. Measurements were performed behind a buildingin a relatively open terrain. The vv-polarized radar return fromthe foliage-obscured vehicle was about 10 dB lower than the re-turn from the foliage itself. This was due in part to the high atten-uation rate through the dense white pine cover and due to the factthat significant portions of the vehicles outer frame were com-posed of nonmetallic material. Nevertheless, the vehicle radarresponse enjoyed 20-dB SNR. Despite the vehicles low RCSin comparison to foliage, the normalized coherence parameterwas able to establish the presence of the target behind foliageafter only 11 radar scans and under a low-speed variable wind,as shown in Fig. 14. As can be scene in Fig. 14, the averagenormalized coherence parameter of the wind-influenced foliageexhibited a reduction in magnitude over the various resolutioncells covering the foliage region, while the cells belonging tothe vehicle maintained high values of [magnitude of 1 formost cells (0 dB)], thereby indicating the presence of a large sta-tionary object behind the trees. At the center of the scan (i.e., ata cross range of 0 m), the absolute value of , averaged over11 scans, was 0.999 and 0.668 for the vehicle and the foliage di-rectly in front of it, respectively. The RCS target-to-clutter (veg-etation) ratio , was also computed using the power of the meanfield (radar images are not shown in this paper due to size lim-itations). In case of a single scan, was found to be dB,and in case of 11 averaged scans, the value of was found tobe dB, an improvement of about 3 dB.

It should be noted that in the case of a moving target, temporaldecorrelation of the targets radar return might occur, which inturn could lead to a missed detection if the proposed techniqueis used. As mentioned before, temporal decorrelation can occurif the targets orientation and/or range with respect to the radarchanges while the radar system performs repeated scans of thetarget scene.

IV. TARGET SIGNATURE RETRIEVAL

Polarimetric distortions incurred by foliage on the radar sig-nature of an obscured target can be removed, provided that mul-tiple scans of the target scene are performed. Consider the casedepicted in Fig. 2(b) where the measured radar returns from thedifferent range-bins spanning the scattering canopy were col-lected. By applying first the coherent averaging technique onthe measured returns, the target can be detected, and its posi-tion resolved from that of foliage. Then, the measured return canbe reprocessed one more time by averaging the power (not thecomplex fields) at every range-bin over all scans. The averagedpower can then be used to determine the polarimetric distortionsincurred by foliage, and those distortions can then be removed.

Let the foliage occupy several range-bins, beginning atrange-bin 1 and ending at range-bin . Assume that the portionof foliage spanning range-bins 1 to (Fig. 15) can bemodeled with an equivalent lossy layer where only zero- andfirst-order scattering processes contribute to distortions in apropagating wave. It can be shown that the first-order iterative


(a)

(b)Fig. 13. Photographs depicting the outdoor experimental setup used. (a) Sideview of the white pine trees with the vehicle parked behind them and (b) theobject scene as seen by the radar.

Fig. 14. Comparison between the magnitudes (in decibels) of the normalizedcoherence parameter generated from a single radar scan and by averaging 11scans of a foliage-obscured vehicle. The absolute value of , averaged over11 scans, for the vehicle and the foliage at cross range = 0 m, was 0.999 and0.668, respectively.

solution of the radiative transfer equations [14] describing theproposed two-layer medium (shown in Fig. 15) reduces to

(7)where is the average Mueller matrix of foliage inrange-bin (the Mueller matrix is a 4 4 matrix that provides

Fig. 15. Two-layer model of foliage.

Fig. 16. Comparison between the received vv-polarized power of a singleradar scan before and after compensation to foliage attenuation was applied tothe dihedrals radar returns. The two images have the same scale in decibels.

a complete description of the polarimetric scattering behaviorfor statistically homogeneous clutter; for details, refer to [14]),

is the averaged measured radar return (4 4 matrix),is the thickness of the distortion layer, and and are4 4 extinction matrices that characterize the polarimetricdistortions incurred by a wave propagating through the foliageup to the th range-bin in the forward and backward directions,respectively. It can be shown through the application of thereciprocity theorem that the two extinction matrices are notindependent, and their elements can related to each other. Ageneral expression for can be found in [14].

Assuming azimuthal symmetry of the foliage canopy, the av-eraged Mueller matrix of the foliage first and thrange-bins can be assumed equal . In this case,

can be easily extracted from the radar return (no distor-tions) and substituted into (7) to invert for the elements of .Once are known, they can be applied to the measured returnfrom the obscured target to retrieve its true radar signature.

Retrieval of the polarimetric signature of the obscured targetrequires an inversion of the expression in (7), which is the sub-ject of further research that will not be addressed in this paper.Instead, only the scalar case will be examined here. In the scalar


Fig. 17. Comparison between a single radar scan before and aftercompensation to foliage attenuation was applied to the vehicles radarreturn. The two images have the same scale in decibels. The value of T ,computed at the center of the scan (cross range = 0 m), increased from5:46 dB for a single scan to +7:14 dB after compensating for foliageattenuation.

case, only the copolarized components, such as will be con-sidered. The expression in (7) reduces in the scalar case to

(8)where here stands for the mean received power fromfoliage at the first range-bin, and is the measured powerat the th range-bin. The value of can be easily computedfor each scan angle in azimuth and then used to correct for theattenuation of the foliage layer. This technique was applied tothe measurements of the trihedral positioned behind the ficustree shown in Fig. 4. After compensating for the deduced valueof in the radar return from the trihdral, the corrected RCS ofthe trihedral was found to be only 2 dB below its theoreticalvalue.

The scalar version of the proposed technique was also ap-plied to the data collected for the dihedrals shown in Fig. 9, andthe corrected radar image is shown in Fig. 16. In this figure, theoriginal image of a single radar scan is plotted along with the at-tenuation-corrected image of the same radar scan. Both imageshave the same scale. As can be seen from the figure, about 20-dBincrease in the dihedrals RCS was realized by the application ofthe technique. Recall that the values of based on single scandata for dihedrals 1, 2, and 3 (shown in Fig. 11) were 0.5, 0.0,and 6.2 dB, respectively. After compensating for foliage atten-uation, the values of for dihedrals 1, 2, and 3 became 19.7,22.0, and 18.3 dB, respectively.

The same approach was applied to the vehicle parked behinda line of small white pine trees shown in Fig. 13. In Fig. 17,the radar return from a single radar image is compared to thecorrected image after estimating the attenuation through foliageand compensating for it in the vehicles return. As mentionedbefore, the value of based on single-scan data of the foliage-obscured vehicle was 5.46 dB. After compensating for foliageattenuation, the value of increased dramatically to 7.14 dB,

and improvement of about 12.5 dB. In computing , target andclutter data at the center of the scan (i.e., at cross range m)were used.

V. CONCLUSION

The effects of wind on the MMW radar return of a foliage-ob-scured target was demonstrated through a set of indoor exper-iments. It was demonstrated that by coherently averaging mul-tiple radar scans of the foliagetarget area, the obscured targetcould be resolved from foliage. In addition, a technique was de-veloped to determine the distortions introduced to the targetssignature due to the foliage and to minimize them. The tech-nique was tested on the simple case of a trihedral obscured by asmall tree and was able to predict the correct value of the atten-uation caused by the tree. The technique was equally successfulwhen applied to outdoor measurements of a vehicle parked be-hind a line of small pine trees.

REFERENCES[1] S. Ayasli and L. Bessette, UHF & VHF SAR phenomenology, in Proc.

PIERS, Workshop on Advances in Radar Methods, Baveno, Italy, Jul.2022, 1998.

[2] B. T. Binder, M. F. Toups, S. Ayasli, and E. M. Adams, SAR foliagepenetration phenomenology of tropical rain forest and northern U.S.forest, in Proc. IEEE Int. Radar Conf., May 811, 1995, pp. 158163.

[3] L. Carin, N. Geng, M. McClure, J. Sichina, and L. Nguyen, Ultra-wide-band synthetic-aperture radar for mine-field detection, IEEE AntennasPropag. Mag., vol. 41, no. 1, pp. 1833, Feb. 1999.

[4] B. Ferrell, Ultrawideband foliage penetration measurements, in Proc.IEEE National Radar Conf. , Mar. 2931, 1994, pp. 8084.

[5] L. Happ, K. A. Kappra, M. A. Ressler, J. P. Sichina, K. Sturgess, andF. Le, Low-frequency ultra-wideband synthetic aperture radar 1995boomsar tests, in Proce. IEEE National Radar Conf., Ann Arbor, MI,May 1316, 1996, pp. 5459.

[6] R. M. Narayanan, D. W. Doerr, and D. C. Rundquist, Temporal decor-relation of X-band backscatter from wind-influenced vegetation, IEEETrans. Aerosp. Electron. Syst., vol. 28, no. 2, pp. 404412, Apr. 1992.

[7] A. Y. Nashashibi, K. Sarabandi, P. Frantzis, R. D. De Roo, and F. T.Ulaby, An ultra-fast wideband MMW polarimetric radar for remotesensing applications, IEEE Trans. Geosci. Remote Sens., vol. 40, no.8, pp. 17771786, Aug. 2002.

[8] A. Y. Nashashibi, K. Sarabandi, S. Oveisgharan, M. C. Dobson, W.Walker, and E. Burke, Millimeter-wave measurements of foliage atten-uation and ground reflectivity of tree stands at nadir incidence, IEEETrans. Antennas Propag., vol. 52, no. 5, pp. 12111222, May 2004.

[9] A. Y. Nashashibi and F. T. Ulaby, Millimeter-wave radar detectionof partially obscured targets, in Digest IEEE AP-S Int. Symp. andUSNC/URSI National Radio Science Meeting, Boston, MA, Jul. 913,2001.

[10] A. Y. Nashashibi, F. T. Ulaby, P. Frantzis, and R. D. De Roo, Measure-ments of the propagation parameters of tree canopies at MMW frequen-cies, IEEE Trans. Geosci. Remote Sens., vol. 40, no. 2, pp. 298304,Feb. 2002.

[11] K. Sarabandi, A. Nashashibi, and E. Burke, Detection of hard targetscamouflaged under foliage using millimeter-wave radars, in DigestIEEE AP-S Int. Symp. USNC/URSI Radio Science Meeting, Columbus,OH, Jun. 2227, 2003, p. 280.

[12] K. Sarabandi and A. Y. Nashashibi, Phenomenology of millimeter-wave signal propagation and scattering for detection of targets camou-flaged under foliage, in Proc. IGARSS, Toulouse, France, Jul. 2125,2003.

[13] T. B. A. Senior, K. Sarabandi, and F. T. Ulaby, Measuring and modelingthe backscatter cross section of a leaf, Radio Sci., vol. 22, no. 6, pp.11091116, Nov. 1987.

[14] F. T. Ulaby and E. C. Elachi, Radar Polarimetry for Geoscience Appli-cations. Norwood, MA: Artech House, 1990.

[15] F. T. Ulaby, K. Sarabandi, and A. Y. Nashashibi, Statistical propertiesof the Mueller matrix of distributed targets, Proc. Inst. Elect. Eng. F,vol. 139, no. 2, pp. 136146, Apr. 1992.


Adib Y. Nashashibi (S82M95SM01) receivedthe B.Sc. and M.Sc. degrees from Kuwait University,Kuwait City, in 1985 and 1988, respectively, and thePh.D. degree in electrical engineering from the Uni-versity of Michigan, Ann Arbor, in 1995, all in elec-trical engineering.

He is currently an Associate Research Scien-tist with the Radiation Laboratory, University ofMichigan. His research interests include microwaveand millimeter-wave remote sensing, polarimetricradar calibration and measurement techniques,

bistatic radar phenomenology, electromagnetic wave propagation, and scat-tering in random media.

Fawwaz T. Ulaby (M68SM74F80) received theB.S. degree in physics from the American Universityof Beirut, Lebanon, in 1964, and the M.S.E.E. andPh.D. degrees in electrical engineering from the Uni-versity of Texas, Austin, in 1966 and 1968, respec-tively.

He is the Vice President for Research and WilliamsDistinguished Professor of electrical engineeringand computer science at the University of Michigan,Ann Arbor. His current reserarch interests includemicrowave and millimeter-wave remote sensing,

radar systems, and radio wave propagation. He has authored ten books andpublished more than 500 papers and reports on these subjects.

Dr. Ulaby is the recipient of numerous awards, including the Eta KappaNu Association C. Holmes MacDonald Award as An Outstanding ElectricalEngineering Professor in the United States of America for 1975, the IEEEGeoscience and Remote Sensing Distinguished Achievement Award in 1983,the IEEE Centennial Medal in 1984, The American Society of Photogram-metrys Presidential Citation for Meritorious Service in 1984, the NASAGroup Achievement Award in 1990, and the 2000 IEEE ElectromagneticsAward. He was President of the IEEE Geoscience and Remote Sensing Societyfrom 1980 to 1982, Executive Editor of IEEE TRANSACTIONS ON GEOSCIENCEAND REMOTE SENSING from 1983 to 1985, and as General Chairman ofseveral international symposia. In 1995, he was elected to membership in theNational Academy of Engineering and currently serves as Editor-in-Chief ofthe PROCEEDINGS OF THE IEEE.

tocDetection of Stationary Foliage-Obscured Targets by PolarimetricAdib Y. Nashashibi, Senior Member, IEEE, and Fawwaz T. Ulaby, FeI. I NTRODUCTION

Fig.1. Application of land vehicle-mounted MMW radars to detectFig.2. Presence of foliage in front of an object distorts the tII. W IND -I NDUCED T EMPORAL D ECORRELATION OF F OLIAGE C OVER

Fig.3. (a) Numerically simulated monostatic RCS of two differenFig.4. Indoor experimental setup to characterize the statisticsA. Indoor Experimental Validation

Fig.5. Normalized temporal decorrelation function for both the Fig.6. Real and imaginary components of the measured complex vvFig.7. PDFs of the measured vv-polarized backscatter power fromIII. T ECHNIQUE TO R ESOLVE T ARGET F ROM F OLIAGEA. Detection Features

Fig.8. Real and imaginary components of the complex coherence pFig.9. Indoors experimental setup whereby three dihedrals were B. Imaging of Foliage-Obscured Point Targets: Indoor Case

Fig.10. Photographs depicting the indoors experimental setup usFig.11. Comparison between a single-scan radar image and the raFig.12. Comparison between the magnitudes (in decibels) of the C. Imaging of Foliage-Obscured Stationary Target: Outdoor CaseIV. T ARGET S IGNATURE R ETRIEVAL

Fig.13. Photographs depicting the outdoor experimental setup usFig.14. Comparison between the magnitudes (in decibels) of the Fig.15. Two-layer model of foliage.Fig.16. Comparison between the received vv-polarized power of aFig.17. Comparison between a single radar scan before and afterV. C ONCLUSIONS. Ayasli and L. Bessette, UHF & VHF SAR phenomenology, in Proc.B. T. Binder, M. F. Toups, S. Ayasli, and E. M. Adams, SAR foliaL. Carin, N. Geng, M. McClure, J. Sichina, and L. Nguyen, Ultra-B. Ferrell, Ultrawideband foliage penetration measurements, in PL. Happ, K. A. Kappra, M. A. Ressler, J. P. Sichina, K. SturgessR. M. Narayanan, D. W. Doerr, and D. C. Rundquist, Temporal decoA. Y. Nashashibi, K. Sarabandi, P. Frantzis, R. D. De Roo, and FA. Y. Nashashibi, K. Sarabandi, S. Oveisgharan, M. C. Dobson, W.A. Y. Nashashibi and F. T. Ulaby, Millimeter-wave radar detectioA. Y. Nashashibi, F. T. Ulaby, P. Frantzis, and R. D. De Roo, MeK. Sarabandi, A. Nashashibi, and E. Burke, Detection of hard tarK. Sarabandi and A. Y. Nashashibi, Phenomenology of millimeter-wT. B. A. Senior, K. Sarabandi, and F. T. Ulaby, Measuring and moF. T. Ulaby and E. C. Elachi, Radar Polarimetry for Geoscience AF. T. Ulaby, K. Sarabandi, and A. Y. Nashashibi, Statistical pro

detection of stationary foliage-obscured targets by polarimetric millimeter-wave radar

Documents