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2104 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 6, JUNE 2016

Efficient and Robust RFI ExtractionVia Sparse Recovery

Lam H. Nguyen and Trac D. Tran, Fellow, IEEE

Abstract—This paper presents a simple adaptive frameworkfor robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB)radar signals in challenging bandwidth management environ-ments. RFI sources pose critical challenges for UWB systems since1) RFI often occupies a wide range of the radar’s operating fre-quency spectrum; 2) RFI might have significant power; and 3) RFIsignals are difficult to predict and model due to the nonstation-ary nature as well as the complexity of various communicationdevices. Our proposed framework involves an initial RFI estima-tion step that operates directly on already contaminated radarsignals to identify RFI-dominant frequency sub-bands. This vitalprior information is then taken into account to construct an adap-

tive RFI dictionary with various sinusoidal patterns covering theaforementioned RFI-contaminated frequency spectrum. Finally,we employ a sparsity-driven optimization strategy to separate andthen extract RFI from the received radar signals. Our method canbe implemented as a denoising preprocessing stage for raw radarsignals prior to image formation and other follow-up tasks such astarget detection and classification. Recovery results from extensivesimulated data sets as well as real-world signals collected by theU.S. Army Research Laboratory (ARL) UWB synthetic apertureradar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.

Index Terms—Compressed sensing (CS), interference, noise–source separation, radar, radio-frequency interference (RFI),sparse recovery, sparsity, synthetic aperture radar (SAR), ultra-

wideband (UWB).

I. INTRODUCTION

I N THIS paper, we are interested in ultra-wideband (UWB)radar systems that transmit signals spanning a wide fre-

quency spectrum from under 100 MHz to several GHz, deliver-ing penetration capability while maintaining high image resolu-tion [1]–[8]. For example, the U.S. Army Research Laboratory(ARL) has been developing low-frequency UWB radar systemsto detect difficult targets in various applications such as foliagepenetration (FOPEN) [9], ground penetration for improvisedexplosive device (IED) detection [10], and sensing-through-the-wall (STTW) [11]. A critical challenge for UWB radars is that

collected radar information is very susceptible to corruptionby radio-frequency interference (RFI) signals within the hugeoperating spectrum since the radar signal spectrum in this case

Manuscript received October 02, 2015; revised February 04, 2016; acceptedFebruary 06, 2016. Date of publication February 28, 2016; date of currentversion July 05, 2016.

L. H. Nguyen is with the RF Signal Processing and Modeling Branch,U.S. Army Research Laboratory, Adelphi, MD 20783 USA (e-mail:[email protected]).

T. D. Tran is with Department of Electrical and Computer Engineering, JohnsHopkins University, Baltimore, MD 21218 USA (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2016.2528884

contains significant overlaps with those of radio, TV, cellularphone, wireless networking, amateur radio, etc., resulting inseverely reduced signal-to-noise ratio (SNR) and ultimatelyreducing the effectiveness of target detection/classification. Theobserved received radar signal at aperture ith can often be mod-eled as a linear combination of the true backscattered radarsignal, the RFI signal, and the typical unstructured dense noisewith small variance (e.g., atmospheric interference and thermalnoise from transmitter/receiver circuits). Since the interferenceis often dominated by various modulation schemes popular inwireless broadcasting and communication, the received sig-

nal contains spectral content that includes many frequencysub-bands that are corrupted by energy from all other RFIsources. Within these corrupted sub-bands, the energy of thereceived signal can be much smaller than that from the interfer-ence sources, since the interfering signals are essentially largeamplitude noise that often masks the underlying radar signals.Alternatively, from the time-domain viewpoint, the signal isvery noisy and might be embedded in the noise floor. Except fortargets with very large amplitudes, targets may not be detectablein the presence of interference noise.

Mitigation of RFI is a notoriously challenging problem dueto the dynamic and unpredictable nature of the noise sources,

not to mention the strength of the noisy signals. Previous workin this RFI-mitigation area can be classified into two categories:1) RFI suppression via filtering techniques, where estimatedRFI sources are filtered out or suppressed under the noisefloor; and 2) RFI extraction, where RFI components are firstidentified, estimated, and then subtracted out of the observedsignals. Following the former approach includes notch filtering,sub-band filtering, and/or adaptive filtering techniques, whichare popular in practical implementations due to its simplicity[12]–[17]. However, suppression of RFI via notching/filteringadversely affects the strength of synthetic aperture radar (SAR)signals, as well as introduces significant sidelobes, leading tosevere ringing problems in the final SAR image.

The latter RFI-extraction approach comprises techniquesemploying parametric noise modeling [18], spectral decompo-sition [19], independent component analysis (ICA) [20], [21],and eigen decompositions [21]–[23]. The main difficulty hereis that collected data are already heavily contaminated withRFI. Hence, unless very accurate prior information on RFIsources exists, it is often very difficult to effectively model orestimate (and then separate) RFI from the desired SAR sig-nals. Most of these algorithms can only provide acceptableresults with one particular source of RFI and very restrictiveassumptions (e.g., very narrow corrupted RFI sub-bands) duevarious difficulties in modeling complicated RFI noise sources.

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NGUYEN AND TRAN: EFFICIENT AND ROBUST RFI EXTRACTION 2105

For instances, several past efforts have taken advantage of thelow-rank or narrowband RFI property to extract them via ICAor eigen decompositions [20]–[23]. However, these principal-component-based techniques heavily depend on the quality of the orthogonal subspaces and cannot distinguish signal versusnoise if they happen to have the same power within the samesubspace.

The recently emerging theory of compressed sensing (CS)[24]–[27] stimulates numerous investigations on the applicabil-ity of sparsity in radar imaging [28]–[41]. Most sparsity-basedapproaches tend to focus on obtaining sparse scenes; hence,they involve computationally expensive optimization algo-rithms with the image formation step embedded right withinthe main iterative loop. As a result, these algorithms are notdirectly applicable to UWB high-resolution SAR applications,where sampling rates are often in the GHz range, scene reso-lution is usually in the submeter range, and hundreds of datarecords need to be collected and processed every second.

Our previous attempt in solving the RFI problem follows

the RFI-extraction approach via sparse recovery and requiresprior knowledge of RFI via sniffing [40], [41]. Unfortunately,even this promising technique has one significant drawback:the radar system has to continuously monitor the surroundingenvironment in order to build a representation dictionary forthe interference sources. We have suggested turning the radartransmitter OFF occasionally while leaving the receiver ON asa simple and quick solution. This way, whenever the radar sys-tem is in the sniffing stage, i.e., the transmitter is turned OFF,the receiver would collect only the vital information on theinterference (since there is no radar signal present, what wesense must be pure interference). However, even this simple

“sniffing” solution 1) increases the complexity of the systemcontrol and 2) reduces the system’s effective pulse repetitionfrequency (PRF). Obviously, if we desire to capture the interfer-ence characteristics accurately, then we would need to increasethe sniffing frequency and the system’s PRF would decreasesignificantly. On the other hand, if we try to minimize theamount of sniffing, then the interference modeling will not beas precise, rendering our proposed solution to be much lesseffective.

In this paper, we further simplify this RFI-suppression tech-nique and demonstrate that the RFI problem can still beeffectively solved without any cumbersome/costly effort in col-lecting the prior RFI information. Our main novel contributions

are listed as follows.1) Spectral locations of potential RFI contamination are

estimated efficiently and directly from noisy observedsignals.

2) Based on the RFI-estimated knowledge above, an adap-tive RFI sparsifying dictionary is constructed to accu-rately capture RFI components.

3) The proposed algorithm can be thought of as an adaptivenonblind noise–source separation strategy relying heav-ily on two valuable sources of prior information to obtainseparation: we have direct knowledge of the transmittedSAR signal (allowing us to build the sparsifying SAR

dictionary), and we extract RFI knowledge from past cor-rupted observations (allowing us to build the sparsifying

RFI dictionary). As long as the SAR signal and the RFIhave a reasonable degree of separation, our proposedalgorithm should remain effective.

4) Simple fast recovery techniques such as matching pursuitare employed to perform effective noise–source separa-tion. Extensive experiments with simulated and real SARdata sets as well as RFI data confirm that the proposedapproach significantly outperforms the standard RFI fil-tering technique by a large objective as well as subjectivemargin across the entire tested range of RFI bandwidthand power.

5) Our method can be easily incorporated into most existingsystems as a preprocessing module prior to other pop-ular signal processing and image formation steps sincethe proposed technique allows the processing of receivedradar signals in independent fashion, in parallel, anddirectly in the raw sample domain.

Organization wise, Section II provides details on ourapproach in sparse radar data modeling, the choice of appropri-

ate sparsifying dictionaries, and the associated sparse recoveryalgorithms. Section III presents our main proposed approach forextraction and suppression of RFI signals from UWB radar sig-nal applications. Section IV presents simulated UWB data andreal data collected from the ARL BoomSAR radar [7], whichare employed in various experiments to confirm the validityof the proposed approach and associated optimization algo-rithms. Finally, a brief summary and discussion of future workin Section V conclude the paper.

Throughout this paper, we use bold-faced lowercase char-acters to denote vectors and bold-faced uppercase charactersto denote matrices. The l p-norm of the vector α is defined

as α p = p

i|αi|

p

, whereas the l0-norm of α is simply

defined as its number of nonzero (significant) elements. An N -sample signal x is called sparse if there exists a sparsifyingbasis or dictionary D such that we can represent x as x = Dα

with α0

= S << N , i.e., the number of basis vectors (oratoms) needed to represent x is significantly lower than itsdimension. In practice, x is often just approximately sparse—we can achieve an acceptable fidelity in the representation of x with a reasonable range of sparsity level, i.e., x ≈Dα withα

0 ≈ S << N .

II . BACKGROUND: UWB SAR SYSTEMS, SPARSE S IGNAL

MODELING, A ND S PARSE RECOVERY W IT H RFI

A. UWB Radar Signal Model and Sparse Signal

Representation

Let us consider a simplistic impulse-based SAR system try-ing to capture a simple scene with only two significant targetsas depicted in Fig. 1. At aperture i, the transmitter would trans-mit a probing pulse s and the receiver would record the echoedbackscattered signal yi. In the ideal case with exactly two pinttargets and without any interference, we expect the receivedsignal to contain two bounced-back pulses indicating how far

apart our targets are with respect to the sensing platform. Notethat this example illustrates precisely an UWB radar system




Fig. 1. Simple illustration of an impulse-based UWB SAR and its receivedsignals with and without RFI in both the time and frequency domain.

since the impulse in time leads to a wideband frequency rep-

resentation. In practice, UWB radar signals are often interferedby wireless signals ri from AM/FM radios, TV broadcast-ing, mobile phone communications, etc. At each aperture, weobserve a similar SAR signal characteristic: occasional narrowpulses indicating the presence of significant targets of interestembedded in a sea of RFI waves. If the RFI power is strongenough, these SAR spikes might be completely lost in the inter-ference waves and we cannot visually detect them anymore. Inshort, SAR signals are sparse in the time domain, whereas RFIsignals are sparse in the frequency domain. They come fromcompletely different independent sources, and they behave verydifferent statistically. This is the most critical assumption in theproposed method.

We propose the following sparse-signal, sparse-noise modelfor the received radar signal yi:

yi = xi + ri + wi = DSARαi + DRFI

i ei + wi. (1)

In our model, the original SAR signal xi at the ith aperture isassumed to be sparse with respect to DSAR—the phase-shifteddictionary constructed from the knowledge of our transmittedsignal s. The received signal yi is often contaminated by var-ious different noise sources, modeled here as the RFI ri andthe dense white noise wi. The latter noise component is thecommon thermal, atmospheric, mechanical noise that exists inany radar and communication system. It is usually modeled as

dense Gaussian white noise that is fortunately negligible mag-nitude wise. In other words, wi in (1) has small bounded energywi2 ≤ σ. The former noise component is the RFI that we arereally after. The main difference between the two noise sourceshere is that ri is sparse with a properly designed RFI noise dic-tionary and can be captured with only a few significant entriesbut each can be large in magnitude, whereas wi is dense andgenerally insignificant in magnitude.

Most previous work in the literature on applying CS andsparse recovery techniques to radar applications relies on thefar-field assumption and the sparseness of the scene of interest.Hence, the problem formulations and the resulting algorithms

focus mainly on sparse SAR images [28]–[35], [38], [39]. Onthe contrary, our approach is to target the raw data directly, right

at the prefocused stage, as a preprocessing step. In other words,the sparse signal model we employ throughout this paper forour raw SAR-received data record is

xi =

jαijsj (2)

where xi is the digitized observed signal collected from the

SAR receiver’s analog-to-digital convertor (ADC) at the ithaperture, sj is the digitized transmitted probing signal delayedby various amount s (t − τ j), and αij is the scalar factor rep-resenting signal attenuation at aperture i associated with theprobing signal with time-shift parameter τ j . Here, we model thereceived SAR signal as the summation of all reflections fromall targets within the area of interest. Hence, the received signalis composed of a linear combination of delayed and weightedreplicas of the transmitted pulse, and we propose to capture thereceived signal x as follows:

xi = DSARαi (3)

where the matrix DSAR is the redundant overcomplete dictio-nary whose columns are time-shifted digitized version of thetransmitted signal s(t) as previously described. Each column(atom) of the time-shifted dictionary DSAR can be thought of as the discrete version of the analog signal s (t − kτ ) , k ∈ Z,where the parameter τ determines the resolution of the system(the smaller it is, the more accurate the following-up imagingstage at the expense of more redundancy and a larger sparsi-fying dictionary). The column vector αi is the sparse vectorof weighting coefficients. Significant elements in αi indicatethe existence of significant object(s) in the scene. The positionand magnitude value of significant coefficients in αi reveal the

potential position and shape information of those significant tar-gets. The record xi is sparse in the dictionary DSAR in thesense that few significant coefficients in αi correspond to sig-nificant targets or objects within that local neighborhood. It isimportant to note that although the observed scene might becomplex with many objects, the complexity K of the receivedata record is significantly reduced since the reflections fromall targets that have the same range distance from the radartransmitter/receiver would be represented by a single reflec-tion coefficient and phase. In addition, the return from a targetis typically dominated by a few primary scatterers (large tar-gets with high reflective indices). Finally, in this paper, we haveassumed that the same transmitted pulse is employed through-

out, leading to the same dictionary DSAR for all apertures, andwe have ignored the subscript i. If the transmitted pulse is var-ied adaptively as in [42] and [43], the corresponding sparsifyingdictionariesDSAR

i should be modified accordingly. On the con-trary, our RFI dictionary DRFI

i discussed in Section II-B needsto be adaptive for each aperture.

In this basic model, each data record collected at each syn-thetic aperture i is represented and recovered independently.Moreover, the sparsity level S in the sparse representationabove is directly related to the complexity level of the scene.If S is kept fixed throughout all data records, then we are onlyinterested in, at most, S significant point targets in any partic-

ular scene. The reader should also note that our model in (2)covers the practical scenario where the exact sparsity level S




Fig. 2. Typical example of a modern-day RFI spectrum. Top left: RFI spectrum with the red line depicting the level of RFI suppression via the notching schemedescribed in Section IV, resulting in roughly 10% spectrum loss. Top right: two RFI segments in time domain 1 ms apart. Bottom left: auto-correlation of normalized RFI within a 2-ms window. Bottom right: spectrum of two RFI segments 1 ms apart above.

does not exist or it is unknown beforehand. In this case, thesmaller-magnitude coefficients αij are simply discarded and areconsidered as part of the noise component wi. Finally, notethat our formulation here is for impulse radars. Other UWBSAR configurations such as stepped-frequency and chirp radarsneed an extra simple preprocessing step prior to our RFI extrac-tion. The interested reader is referred to [1] for more detaileddiscussions on UWB radar technologies.

B. Sparse Recovery

As discussed in Section II-A, we can model the data obser-vation process as y = DSAR

α. If we expect to observe onlya few S point targets in a scene of much higher complex-ity N , then we have a linear inverse problem with a sparsityconstraint. Hence, the locations as well as the amplitudes of these S point targets can be uniquely recovered from only M

observations y ∈ M or C M (S << M < N ) via solving thefollowing sparsity-driven 0-minimization problem:

Noiseless: α̂ = arg minα

α0

s.t. y = DSARα

Noisy: α̂ = arg minα

α0 s.t.y−DSARα

2 ≤ σ. (4)

It has been proven in the CS literature that if the fat M × N

matrix DSAR obeys the restricted isometry property (RIP) [25],then we can solve the following 1-minimization problem andwould still be able to obtain exactly the same optimal S -sparsesolution α̂ as long as the number of measurements is on theorder of M = S log N [24]–[27]:

Noiseless: α̂ = arg minα

α1

s.t. y = DSARα

Noisy: α̂ = arg minα

α1

s.t.y−DSAR

α

2 ≤ σ. (5)

In a more practical setting where we often have todeal with imperfect observations contaminated by noise, i.e.,

y = DSARα + w, where w is some unknown perturbation

(noise) bounded by a known amount w2

< σ and/or α mightnot be exactly S -sparse, the optimization problem in (5) is oftenconverted to the following convex formulation:

α̂ = arg minα

y−DSARα

2

+ λα1 (6)

where the first term in the cost function enforces the data consis-tency, whereas the second term can be considered as the sparsityprior and the parameter λ provides the tradeoff between thetwo [44]–[46]. Another popular approach in sparse recovery isto approximately solve the 0-minimization problem in (4) viavarious matching pursuit strategies [47]–[50]. In this case, theoptimization setup is as follows:

α̂ = arg minα

y−DSARα

2

s.t. α0 ≤ S. (7)

The next question is: what if the interference has significantenergy and is not bounded as the RFI component modeled inSection II-A.

C. RFI Characteristic

Unlike our other noise source wi, the RFI sources ri buriedin our observed signal yi are not completely random. RFI hasvery strong structured components and often contains a highlevel of energy. Fig. 2 presents a snapshot of a modern-day RFIspectrum captured from the rooftop of our ARL building usingARL’s own radar receiver (with the transmitter turned OFF).As one can observe, although there are multiple interferencesources ranging from AM to FM radio, from digital TV broad-cast to cellular phone communications, the RFI turns out to bequite sparse in frequency. Let r [n] be the many collected seg-

ments of RFI in the time domain; Fig. 2 (bottom left) showsthe autocorrelation sequence c [m] ≡ E (r [n] r∗ [n − m]) with




Fig. 3. Identification of RFI-contaminated frequency sub-bands from noisy corrupted observed data. The RFI here is a random RFI segment selected from thesame analytical experiment described in Section II-C. It is also normalized to have the same power as the SAR signal.

the lag coefficient m in the ±1 ms range. We can see thatthe RFI signal is relatively stationary with a steady decreasein magnitude of the correlation coefficient. Fig. 2 also illus-

trates that the majority of the RFI power concentrates withinthe top 10% of its components. Moreover, although the two RFIsegments in the time domain appear to have nothing in com-mon, their frequency spectra seem almost identical. If observedradar signals are collected and processed within a small spatial-temporal neighborhood, we believe that the frequency-sparseand frequency-stationary assumptions on RFI sources arevalid.

III. SPARSITY-DRIVEN ESTIMATION

AN D EXTRACTION OF RFI

A. RFI Spectrum Estimation From Received RFI-

Contaminated Radar Signals

RFI sources typically are frequency sparse compared tothe full bandwidth of the radar signals. The frequency-sparsefeature of RFI can be easily explained: most modern communi-cation systems rely on modulation to various higher frequenciesfor data transmission and broadcasting, and each system typi-cally occupies only a few MHz of the spectrum. We hypothesizethat the RFI noise sources ri can be captured effectively withits own sparse representation as ri = DRFI

i ei, where DRFI i is

the adaptive RFI noise sparsifying dictionary constructed fromcosine and sine waveforms.

The first step to construct the RFI dictionary is to exploitimportant prior knowledge obtained from an RFI spectrum esti-mator. This system component takes advantage of the long-termfrequency correlation structure of common RFI sources to pro-vide a rough estimate of frequency bands that are most likelyto contain RFI. One particular solution, as shown in Fig. 3, isto average the spectrum of the received radar signals yi overmany apertures within a reasonable spatial-temporal window.More precisely, when processing the received radar signal yi ataperture position indexed by i, a certain 2 A number of neigh-boring previously received signals can be borrowed to formthe estimated local spectrum average Y

averagei (ω) at the ith

location as

Y averagei (ω) =

Ak=−A

|Y i+k (ω)|2

=A

k=−A

|X i+k (ω) + Ri+k (ω) + W i+k (ω)|2

≈A

k=−A

|X i+k (ω) + Ri+k (ω)|2

≈A

k=−A

|X i+k (ω)|2 + |Ri+k (ω)|2 (8)

where we have made the following two assumptions: 1) thepower of the bounded noise component |W i+k (ω)|2 is insignif-icantly small and 2) the SAR and RFI components {xi, ri} of

the received signal yi have negligible cross correlation. Thisaveraging operation in the frequency domain yields a typi-cal spectrum as illustrated in Fig. 3. Most importantly, theaveraging process above identifies the location of the mostpersistent RFI sources as local peaks (maxima) in the over-all observed spectrum due to the energy contribution from theterms |Ri+k (ω)|2. Note that this operation is locally adaptiveand we can control the sensitivity of the operation from tun-ing the parameter A. If the value of A is too small, we willnot be able to locate and identify frequency bands that con-tain RFI sources of weaker magnitudes. On the other hand,if the value of A is too large, the RFI spectrum estimation ataperture location i will not truly reflect the interference thataffects particularly the received signal at aperture i, and wemight dilute the RFI dictionary in the following dictionary-construction step, which eventually affects the effectiveness oraccuracy of the final sparse-recovery step. Ultimately, the opti-mal setting for A depends on how stationary the RFI sourcesare within a local spatiotemporal neighborhood around the ithaperture. A typical setting for A should cover a few seconds of data.

B. Construction of RFI Dictionary

Next, we construct our estimated RFI dictionary DRFI i from

only sinusoids in frequency bands that have been found to be




prone to RFI (e.g., frequency bands that have been markedby the red asterisks in Fig. 3). The spectrum estimator detectsM RFI frequency bands that are associated with RFI. The jthRFI band ( j = 1, . . . , M RFI ) spans the frequency range fromf L( j) to f H ( j). For each detected RFI frequency band, a num-ber of pairs of sine and cosine tones (waveforms) are generatedto form the subdictionary for this RFI band.

Let the frequency increment between two adjacent tones inthe RFI dictionary be ∆f D, the number of pairs of cosine andsine waveforms to be generated for each RFI band is

N j = round

f Hj − f Lj

∆f D

.

The RFI subdictionary that corresponds to the j th RFI bandfrom f L( j) to f H ( j) is

DRFI ij = [cos(2π(f L( j) + k∆f D))| sin(2π(f L( j) + k∆f D))] ,

k = 1, . . . , N j . (9)

The RFI dictionary for the ith aperture position is theconcatenation of all RFI subdictionaries is

DRFI i =

DRFI ij

, j = 1, . . . , M RFI . (10)

The only parameter in the RFI detection process is ∆f D,which controls the number of atoms/elements (columns of thematrix) in the dictionary. Hence, it also provides the complexityversus performance-accuracy tradeoff in the followed-up sparserecovery algorithms. In this paper, we set it as the inverse of thetotal observation time of one data record.

In summary, the steps to construct the RFI dictionary involve

the following: 1) the RFI spectrum analyzer/estimator estimatesthe frequency bands that the RFI signals occupy as mentionedabove; 2) for each detected RFI frequency band, the subdic-tionary for this RFI band is constructed by generating pairs of sine and cosine waveforms that span the frequencies within thatband; and 3) the RFI dictionary is constructed by concatenat-ing all RFI subdictionaries generated in the previous step. Wechoose pairs of cosine and sine waveforms to ensure that allphases of RFI signals can be accounted for. Also, note that theRFI dictionary DRFI

i is adaptive. Since the detected RFI fre-quency bands are data dependent, the RFI dictionary changeswith respect to time and space. In other words, each aperturethat we are interested in processing has its own RFI dictionary

constructed from received signals within a small spatiotempo-ral neighborhood. Finally, note that this process only providesa rough estimate of the RFI spectrum. More sophisticated tech-niques or better prior information can be taken into account tofurther improve this RFI dictionary.

C. RFI Extraction and Suppression via Sparse Representation

The spectrum estimator detects M RFI frequency bands thatare associated with RFI. The j th RFI band ( j = 1, . . . , M RFI )spans the frequency range from f L( j) to f H ( j). For eachdetected RFI frequency band, a number of pairs of sine and

cosine tones (waveforms) are generated to form the subdic-tionary for this RFI band. Once the two sparsifying dictionaries

are obtained, we proceed to simultaneously seek two sparse rep-resentations at each aperture i: one for the radar signal xi andthe other for the RFI component ri. This leads to the followingoptimization problem:

{α

i, e

i}= arg minαi,ei

{αi0 + ei0} s.t.

yi =DSAR DRFI

i

αi

ei

+ wi. (11)

which can be modified slightly so that it can be approximatelysolved with orthogonal matching pursuit (OMP) [47]

{α

i,e

i} = arg minαi,εi

yi−DSARαi −DRFI

i ei

2

s.t.

αi0 + ei0 ≤ S. (12)

We also implement the following relaxed convex optimiza-tion problem, where λ and τ are tuning parameters that controlthe tradeoffs between the sparsity priors and the observa-

tion consistency constraint, following the alternating directionstrategy as shown in [46]:

{α

i, e

i} = arg minαi,εi

yi−DSARαi −DRFI

i ei

2

+ λαi1 + τ ei1. (13)

Note that in both formulations of (12) and (13), the entries inboth dictionaries should be normalized. Hence, the optimiza-tion is not dependent on the noise energy level if the parametersλ and τ are predetermined appropriately. In this paper, we setλ = τ to the default setting in [46]. However, in practice, wemight have different prior knowledge on SAR and RFI. Thus,fine-tuning parameters λ and τ can probably improve perfor-mance results even further. Also, the competition for sparsitybetween αi and ei encourages a clustering behavior, leadingto the separation between the SAR and RFI components. Theresulting noise-suppressed signal can then be computed as

x

i = yi −DRFI i e

i. (14)

Each data record that is expected to contain the SAR signalof interest is recovered independently. All are then supplied tothe image processor to produce the final SAR image.

IV. EXPERIMENTAL SIMULATION AND R ESULTS

A. Experimental Setup

Our experiments are conducted on an UWB step-frequencysimulated data set as well as on real impulse-based UWBBoomSAR data set collected from the ARL radar [7]. Our simu-lated SAR data set is collected from a monostatic, side-looking,step-frequency SAR setting with 1200 aperture positions in astraight line, imaging a scene with around 36 point targets of various amplitudes located in a uniform rectangular array. Thereal UWB low-frequency BoomSAR data set is collected fromthe ARL UWB low-frequency SAR that transmits impulse radar

signals that generate instantaneous a wide bandwidth that spansapproximately 50–1150 MHz [11]. The UWB BoomSAR is




Fig. 4. RFI suppression performance using simulation data. The RFI signals are randomly generated at each aperture record. In the frequency domain, the RFIsignals occupy approximately 19% of the total SAR bandwidth. The SAR signal to RFI noise ratio in this case is −10 dB. (a) Spectrum of SAR, RFI, and SAR plusRFI signals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise; SNR = 2.4 dB. (d) SAR image after RFI extraction using the proposedtechnique: PG = 17.5 dB; SNR = 19.9 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 1.3 dB; SNR = 3.7 dB.

Fig. 5. RFI suppression performance using simulation data. The RFI signals are randomly generated at each aperture record. In the frequency domain, the RFIsignals occupy approximately 19% of the total SAR bandwidth. The SAR signal to RFI noise ratio in this case is −20 dB. (a) Spectrum of SAR, RFI, andSAR plus RFI signals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise; SNR = −7.9 dB. (d) SAR image after RFI extraction usingthe proposed technique: PG = 20.8 dB; SNR = 12.86 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 9.1 dB;SNR = 1.18 dB.

mounted on a platform that emulates the airborne geometry. Itis configured with two transmitters and two receivers to pro-vide the capability of collecting data in different polarizations.The collected data set used in this experiment is configured inhorizontal transmit, horizontal receive (HH) polarization.

There are also two different RFI noise data sets in our exper-iments: 1) the simulated RFI data set generated from randomlymodulated tones; and 2) the real RFI data set collected from thereal environment with the antenna pointing toward Washington,DC, USA; a typical snapshot of its frequency spectrum is shownin Fig. 2. In this real RFI case, for each aperture location i whereradar data are collected, the noise record is randomly capturedand added to the raw radar data record. We use a simple scalingfactor to control the interference power level.

Our simulated RFI data set presents a more challenging sce-nario. At each aperture position, the RFI signal is simulatedby generating many individual RFI sources (or bands). Each

RFI source is a modulated signal with a bandwidth of 6 MHz,

composed of many tones within its bandwidth. Each tone hasa uniformly distributed random amplitude and phase. Thus, theanalog model for such RFI signal at each aperture record is

r (t) =

M

j=1

N

i=1

Aij cos (2π (f j + ∆f i) t + θij) (15)

where M is the total number of RFI sources across the SARspectrum, N is the number of tones within each RFI source, Aij

is the amplitude of each tone and is uniformly distributed in theinterval [0, A], f j is the start frequency of the j th RFI source,∆f is the frequency spacing between the tones that composean RFI source, and θij is the phase of each tone, which is alsouniformly distributed in the interval [0, 2π].

RFI signals generated using (15) with its contents within itsbandwidth are completely random. It represents a more chal-lenging noise source compared to other practical modulation

schemes such as AM, FM, and/or other digital modulation




Fig. 6. RFI suppression performance using simulation data. The RFI signals are randomly generated at each aperture record. In the frequency domain, the RFIsignals occupy approximately 19% of the total SAR bandwidth. The SAR signal to RFI noise ratio in this case is −30 dB. (a) Spectrum of SAR, RFI, andSAR plus RFI signals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise; SNR = −17.8 dB. (d) SAR image after RFI extractionusing the proposed technique: PG = 24 dB; SNR = 6.2 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 15 dB;SNR = −2.8 dB.

Fig. 7. RFI suppression performance with various RFI to SAR bandwidth ratios and SAR signal to RFI noise ratio using simulation data. The RFI signals arerandomly generated at each aperture record. (a) PG measured in the SAR image domain using the proposed technique. (b) PG measured in the SAR image domainusing the standard notch filtering technique.

sources. Given the RFI to SAR bandwidth ratio, a number of 6-MHz RFI sources are generated across the SAR spectrum. Itis likely that some of them are adjacent to others, forming RFIsources with bandwidths larger than 6 MHz. This is illustratedin Figs. 4–6. In addition, even with an individual RFI source of 6 MHz, the width of its main lobe overlaps a much larger band-width with the SAR data. Again, these simulated RFI signals

are selected at random and added to the original SAR data tosimulate RFI-contaminated signals.To benchmark RFI suppression/extraction performance, we

employ the root-mean-square (RMS) error as well as the SNRbetween the original data records and the recovered onesdirectly in the time domain or between the original SAR imageand the image from recovered signals. The RMS error and SNRcan be computed using the popular formula below (where N isthe number of samples/pixels)

RMS (x) = 1√

N x

2 (16)

SN R (x, x̂) = 20log10RMS

(x

)RM S (x̂− x) . (17)

Fig. 8. RFIsuppression performance using simulation data. The RFIsignals arerandomly generated at each aperture record. In the frequency domain, the RFIsignals occupy approximately 19% of the total SAR bandwidth. The SAR sig-nal to RFI noiseratio inthiscaseis −30 dB. (a) SARimage after RFIextractionusing the proposed technique with OMP solver: PG = 24 dB. (b) SAR imageafter RFI extraction using the proposed technique with L1 ADMM solver:PG = 24.75 dB. Despite the modest objective PG, the L1 solver achieves bet-ter subjective performance than the OMP solver. Several subtle targets are stillvisually detectable using the L1 solver.

We also define the processing gain (PG) as follows (again,

this can be applied to the raw signals as well as to the SARimages):




Fig. 9. RFI suppression performance with simulated SAR signals and real RFI signals. The RFI signals are obtained from actual measurements acquired at the

rooftop of the ARL building. The SAR signal to RFI noise ratio in this case is−

20 dB. (a) Spectrum of SAR, RFI, and SAR plus RFI signals in this experiment.(b) Original SARimage. (c) SARimage with RFInoise. SNR = −7.5 dB.(d) SARimage after RFIextraction using the proposed technique. The PG of 21.97 dB;SNR = 14.4 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG of 12.94 dB; SNR = 5.4 dB.

Fig. 10. RFI suppression performance using data from the ARL side-looking UWB BoomSAR radar and real RFI data. In the frequency domain, the RFI signalsoccupy approximately 18% of the total SAR bandwidth. The signal to RFI noise ratio in this case is −14.7 dB. (a) Spectrum of SAR, RFI, and SAR plus RFIsignals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise. SNR = −9.2 dB. (d) SAR image after RFI extraction using the proposedtechnique: PG = 15.6 dB; SNR = 6.4 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 9.3 dB; SNR = 0.1 dB.

PG = 20 log10 RM S (x)RMS (x̂− x)

− 20log10 RM S (x)RM S (y− x)

(18)

where x is the original SAR signal/image, x̂ is the recoveredSAR signal/image, and y is the observed RFI-contaminatedsignal/image as modeled in (1). This measures in dB the gainthat various RFI-suppression or RFI-extraction algorithms canachieve over the original noisy observations. Again, this PG canbe measured in the raw data domain or in the final SAR imagedomain. Finally, we did not perform any parameter tuning. Infact, our OMP-based algorithm only requires a single param-

eter S (sparsity), which we set at 25% of the received signaldimension.

B. Analysis

Figs. 4–6 depict the results from our first experiment with thesimulated RFI and simulated SAR data. As mentioned above,we simulated the SAR scene using a side-looking SAR geom-etry with the radar platform traveling along a linear path andilluminating a scene with 36-point targets with a wide rangeof amplitudes. The largest targets are calibrated at amplitudesof 0 dB and the smallest at -35 dB. The SAR signals occupya spectrum from 500 to 1500 MHz, with no energy outsidethis band. However, since the energy is tapered at both endsof the spectrum, the effective spectrum of the SAR data spansapproximately from 600 to 1400 MHz. The RFI signal sources(each source has a bandwidth of 6 MHz) are randomly simu-

lated within this effective SAR spectrum. Figs. 4–6 show the




Fig. 11. RFI suppression performance using data from the ARL side-looking UWB BoomSAR radar and real RFI data. In the frequency domain, the RFI signalsoccupy approximately 18% of the total SAR bandwidth. The signal to RFI noise ratio in this case is −21 dB. (a) Spectrum of SAR, RFI, and SAR plus RFI signalsin this experiment. (b) Original SAR image. (c) SAR image with RFI noise. SNR = −15.5 dB. (d) SAR image after RFI extraction using the proposed technique:PG = 20.2 dB; SNR = 5.1 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 11.5 dB; SNR = −3.6 dB.

Fig. 12. RFI suppression performance using data from the ARL side-looking UWB BoomSAR radar and real RFI data. In the frequency domain, the RFI signalsoccupy approximately 18% of the total SAR bandwidth. The signal to RFI noise ratio in this case is −30 dB. (a) Spectrum of SAR, RFI, and SAR plus RFI signalsin this experiment. (b) Original SAR image. (c) SAR image with RFI noise. SNR = −24.6 dB. (d) SAR image after RFI extraction using the proposed technique:PG = 25.3 dB; SNR = 0.7 dB. (e) SAR image after RFI suppression using the standard notch filtering technique: PG = 18.7 dB; SNR = −5.9 dB.

original SAR spectrum, RFI spectrum, and SAR plus RFI spec-

trum. Although the locations of the RFI sources are differentin each case, the total bandwidth of the RFI sources is fixed atapproximately 19% of the SAR bandwidth in this experiment.The SAR signal to RFI noise energy ratios in three cases of thisexperiment are −10, −20, and −30 dB, respectively. From theresults of this experiment (Figs. 4–6), we observed the PG of 17.5, 20.8, and 24 dB using our proposed estimation–extractiontechnique with the three settings of the input SAR to RFI energyratio, comparing to the PG of 1.3, 9.1, and 15 dB using thestandard filtering technique. Figs. 4–6 also show the originalSAR image, the RFI contaminated SAR image, the resultingSAR image after RFI extraction using our proposed technique,and the resulting SAR image after RFI suppression using thestandard notch filtering technique. Visually, the resulting SAR

imagery using our technique is significantly better than that

using the notch filtering technique. Even at the more benign RFIsetting (SAR to RFI ratio of −10 dB) of Fig. 4, the SAR imageusing the notch filter technique [Fig. 4(e)] is very noisy withhigh level of sidelobes that mask many smaller targets. In thiscase, the SAR image using our proposed technique [Fig. 4(d)]is virtually the same as the original image, in which all targetsare visually detectable.

Fig. 7 compares the RFI suppression performance in theSAR imagery domain using the two techniques over a largerange of RFI strengths and bandwidth settings. The PG curvesillustrated in Fig. 7 show that the proposed sparse recoveryframework results in a surprising level of robustness. It consis-tently maintains a high-level RFI suppression regardless of thenoise strength. Even with the very challenging case of an RFI to




Fig. 13. RFI suppression performance using simulation data. The RFI signals are randomly generated at each aperture record. In the frequency domain, the RFIsignals occupy approximately 19% of the total SAR bandwidth. The SAR signal to RFI noise ratio in this case is −30 dB. (a) Spectrum of SAR, RFI, and SARplus RFI signals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise; SNR = −17.15 dB. (d) SAR image after RFI extraction using PCA:PG = 9.04 dB, SNR = −8 dB. (e) SAR image after RFI extraction using the proposed technique: PG = 24.57 dB; SNR = 7.42 dB. (f) SAR image after RFIsuppression using the standard notch filtering technique: PG = 15.12 dB; SNR = −2.03 dB.

Fig. 14. RFI suppression performance using data from the ARL side-looking UWB SAR radar. The signal to RFI noise ratio in this case is −16.36 dB.(a) Spectrum of SAR, RFI, and SAR plus RFI signals in this experiment. (b) Original SAR image. (c) SAR image with RFI noise. SNR = −9.83 dB. (d) SARimage after RFI extraction using PCA: PG = 3.48 dB, SNR = −6.35 dB. (e) SAR image after RFI extraction using the proposed technique: PG = 16.3 dB;

SNR = 6.

46 dB. (f) SAR image after RFI suppression using the standard notch filtering technique: PG = 8.

08 dB; SNR =−

1.

75 dB.

SAR bandwidth ratio of 30%, the proposed technique achievesa PG of approximately 14 dB, regardless of the input RFI level.More importantly, the resulting SAR imagery using our pro-posed technique always visually outperforms the notch filteringtechnique, with many smaller targets still visually detectable.

Between the two sparse recovery algorithms tested, we noticethat, with proper parameter tuning, the l1-based ADMM algo-rithm [46] generally offers a small improvement in objectiverecovery accuracy. However, the l0-minimization OMP algo-rithm [47] is significantly faster: on average, it processes each

SAR data record in under a second, whereas ADMM wouldtake a few minutes to complete the same task. Generally, we

also observe that the S parameter for OMP is a lot less sen-sitive than λ and τ for l1-minimization. From Fig. 8, weobserved a minor difference in performance gain using thetwo algorithms: 24.75 dB for ADMM versus 24 dB for OMP.Visually, the resulting SAR image using the l1-based ADMMalgorithm also performs slightly better than the OMP algo-rithm: a few smaller targets still stand out when using thel1-minimization algorithm. For the rest of the experiments,we decide to rely mainly on OMP due to its efficiency androbustness.

In the experiment of Fig. 9, the simulated SAR signals areinjected with the measured RFI data. Since a significant of




Fig. 15. RFI suppression performance using real data from an airborne UWB SAR radar. (a) Original SAR image with RFI noise. (b) SAR image after RFIsuppression using the notching technique. (c) SAR image after RFI extraction using the proposed technique.

the energy of the measured RFI resides below 500 MHz, weextend the low end of the SAR spectrum to 300 MHz. Thus,in this experiment, the SAR spans a bandwidth from 300 to1500 MHz. Fig. 9 shows that with the SAR to input RFI ratioof −20 dB, we observed the performance gain of approximately22 dB using our proposed technique, which is 9 dB better thanthe notch filtering algorithm.

Figs. 10–12 show the results from our experiment using theARL UWB BoomSAR data with the measured RFI data set.The RFI settings in three cases are −15, −21, and −30 dB,respectively. Fig. 10(b) shows a typical SAR image with afew targets (vehicles) behind foliage and tree area, a manmadestructure, and a calibration target. Figs. 10(c), 11(c), and 12(c)show the same SAR images, which are formed after injectingthe measured RFI data with various energy levels to the rawradar data. In all three cases, we obtain very robust results withthe PGs of 15.6, 20.2, and 24.6 dB. With the SAR to RFI ratioof

−21 dB (Fig. 11), the resulting SAR image using our pro-

posed technique [Fig. 11(d)] is visually close to the originalSAR image with all details are discernable, while the notchfiltering technique [Fig. 11(e)] completely fails and does notretain any detailed features from the scene. It is not surpris-ing that the notch filtering technique completely fails in thiscase. Fig. 11(a) shows that the RFI components are very highcompared to SAR spectrum. Both RFI spectrum and SAR spec-trum have significant overlapping frequency bands. Even thefloor of the RFI spectrum is approximately as high as the SARspectrum. Any notch filter would result in removing signifi-cant energy and bandwidth from the radar signals. Thus, theresulting SAR image would have poor resolution and SNR.

Fig. 12 shows that our proposed technique still performs in thevery challenging level of RFI (SAR to RFI ratio is −30 dB).Although the resulting SAR image of Fig. 12(d) shows that itsnoise floor is obviously higher than that from the original SARimage, it still retains all the resolution and most of the featuresfrom the scene.

Next, for proper benchmarking purposes, we also implementanother RFI extraction algorithm based on eigen decomposi-tion (principal component analysis or PCA) [21]–[23]. Thisis a strategy often mentioned in the RFI suppression com-munity. It has been independently verified by two differentgroups. Although this strategy is based on solid mathematicalfoundation, it has a fundamental problem: there does not exist

any mechanism to distinguish RFI signals from SAR signals

other than signal magnitude. Hence, noise–source separationbecomes very challenging when RFI starts to occupy a sig-nificant portion of the radar operating bandwidth (typicallymore than 10%). Fig. 13 depicts the performance compari-son between PCA, notching, and our proposed sparse-recoverymethod using the simulated signals embedded in simulatedchallenging RFI, whereas Fig. 14 shows the imaging resultsfrom an experiment with real BoomSAR signals embeddedagain in simulated RFI. The reader can immediately observethat PCA performance is lower than that of our method andeven notching, subjectively as well as objectively. We consis-tently observe a similar behavior across a wide range of RFIbandwidth and power settings.

Finally, as a blind test, we apply the proposed RFI extrac-tion algorithm using real data from an airborne UWB SAR.Fig. 15(a)–(c) shows the SAR images of a desert area before andafter RFI processing. The images show a section of dirt roadin the middle of the scene with the road berms clearly shown

its outline. There are two calibration targets (corner reflectors)in the scene, one is in the middle of the road and anotherone in the clutter area. Fig. 15(a) shows that the original RFI-contaminated SAR image is very noisy. The noise backgroundlevels from the road and the clutter areas are similar whereasthe left section of the road is dominated by noise. Even theroad berms are not visible in this section. Fig. 15(b) and (c)shows the same SAR image after the notching, and the pro-posed RFI extraction algorithm is applied to the radar data,respectively. Subjectively, both images show that the responsefrom the road section is much lower than that from the clutterarea, which includes the responses from many trees and bushes.

The discrete responses from the natural clutter objects are sharpand clear. Even the responses from some small natural clutterobjects (e.g., rocks) in the road area are visible. Unfortunately,in this benign RFI case, both algorithms performed similarlywell. Moreover, since there is no available ground truth, wecannot properly evaluate their objective performances.

V. CONCLUSION

We have presented in this paper a simple yet very effectivesparse model that can robustly capture raw UWB SAR datasamples [54]. Our sparse-recovery approach can be thought of as a preprocessing step that accurately estimates RFI sourcesdirectly from contaminated observations. We then perform




source separation via sparse recovery and finally extract theinterference signals before passing the recovered SAR datato the image formation module. We confirm the validity of our framework via numerous experiments using a combi-nation of simulated randomly modulated RFI sources, realmeasured RFI via the ARL radar, simulated step-frequencyUWB SAR systems, and real-world data collected from theARL UWB BoomSAR. The proposed sparse-recovery-based,RFI-extraction technique has shown to be able to preprocessreturned radar data to extract RFI effectively and robustly: itconsistently outperforms the standard RFI filtering techniqueby a significant objective as well as subjective margin acrossthe entire tested range of RFI bandwidth and power. In thefuture, we plan to investigate several joint sparsity and low-rankmodels that can robustly capture the spatial-temporal correla-tion between signals from neighboring SAR apertures to fullyrealize the advantages of two-dimensional (2-D) cross-rangeprocessing. We are currently extending the sparse processingframework described in this paper to the 2-D radar data (both

range and cross-range directions) domain and 2-D SAR imag-ing domain, and assessing the performance gain in variousradar configurations. We also plan to conduct a much morecomprehensive performance comparison between various RFI-mitigation techniques on a diverse set of SAR as well as RFIdata. We anticipate to publish these results in the near future.

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Lam H. Nguyen received the B.S.E.E. degreefrom Virginia Polytechnic Institute, Blacksburg, VA,USA, the M.S.E.E. degree from George WashingtonUniversity, Washington, DC, USA, and the M.S.C.S.degree from Johns Hopkins University, Baltimore,MD, USA, in 1984, 1991, and 1995, respectively.

He started his career with General ElectricCompany, Portsmouth, VA, in 1984. He joined HarryDiamond Laboratory, Adelphi, MD (and its predeces-sor Army Research Laboratory) and has worked there

from 1986 to the present. Currently, he is a SignalProcessing Team Leader with the U.S. Army Research Laboratory, where hehas primarily engaged in the research and development of several versionsof ultra-wideband (UWB) radar since the early 1990s to the present. Theseradar systems have been used for proof-of-concept demonstrations in manyconcealed target detection programs. He has been providing synthetic aper-ture radar (SAR) signal-processing technical consultations to industry for thedevelopments of many state-of-the-art UWB radars. He has been developingalgorithms for SAR signal and image processing. He has authored/coauthoredapproximately 100 conferences, journals, and technical publications. He hasnine patents and other pending patents in SAR signal processing.

Mr. Nguyen has been a member of the SPIE Technical Committee on RadarSensor Technology since 2009. He was the recipient of the U.S. Army Researchand Development Achievement Awards in 2006, 2008, and 2010, the ArmyResearch Laboratory Award for Science in 2010, and the U.S. Army SuperiorCivilian Performance Award in 2011.

Trac D. Tran (S’94–M’98–SM’08–F’14) receivedthe B.S. and M.S. degrees from MassachusettsInstitute of Technology, Cambridge, MA, USA, in1993 and 1994, respectively, and the Ph.D. degreefrom the University of Wisconsin, Madison, in 1998,all in electrical engineering.

In July 1998, he joined the Department of Electrical and Computer Engineering, Johns HopkinsUniversity, Baltimore, MD, USA, where he is cur-rently a Professor. His research interests includedigital signal processing, particularly in sparse repre-

sentation, sparserecovery, sampling, multirate systems, filter banks, transforms,wavelets, and their applications in signal analysis, compression, processing,and communications. His pioneering research on integer—coefficient trans-forms and pre/postfiltering operators has been adopted as critical componentsof Microsoft Windows Media Video 9 and JPEG XR—the latest internationalstill-image compression standard ISO/IEC 29199-2.

Dr. Tran was an ASEE/ONR Summer Faculty Research Fellow at theNaval Air Warfare Center—Weapons Division (NAWCWD), China Lake, CA,USA, in the summer of 2002. Currently, he is a Regular Consultant for theU.S. Army Research Laboratory, Adelphi, MS, USA. He has served as anAssociate Editor of the IEEE TRANSACTIONS ON SIGNAL PROCESSING,the IEEE TRANSACTIONS ON IMAGE PROCESSING, and now the IEEETRANSACTIONS ON C IRCUITS AND S YSTEMS FOR V IDEO T ECHNOLOGY.He was a former member of the IEEE Technical Committee on Signal

Processing Theory and Methods (SPTM TC) and the IEEE Image Videoand Multidimensional Signal Processing (IVMSP) Technical Committee. Hewas the Codirector of the 33rd and 49th Annual Conference on InformationSciences and Systems (CISS’99 and CISS’15), Baltimore, MD, USA. He wasthe recipient of the NSF CAREER Award in 2001, the William H. HugginsExcellence in Teaching Award from Johns Hopkins University in 2007, andthe Capers and Marion McDonald Award for Excellence in Mentoring andAdvising in 2009.

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