Compressive Sensing for Urban Radar

498 Pages·2014·40.047 MB·English

by  Moeness G Amin

Engineering - Electrical Amin
C
Compressive Sensing for Urban Radar
o
m
With the emergence of compressive sensing and sparse signal reconstruc- p
tion, approaches to urban radar have shifted toward relaxed constraints
r
on signal sampling schemes in time and space, and to effectively address
e
logistic difficulties in data acquisition. Traditionally, these challenges have
s
hindered high resolution imaging by restricting both bandwidth and
s
aperture, and by imposing uniformity and bounds on sampling rates.
i
v
Compressive Sensing for Urban Radar is the first book to focus on a
e
hybrid of two key areas: compressive sensing and urban sensing. It

explains how reliable imaging, tracking, and localization of indoor targets S
can be achieved using compressed observations that amount to a tiny
e
percentage of the entire data volume. Capturing the latest and most
n
important advances in the field, this state-of-the-art text:
s
• Covers both ground-based and airborne synthetic aperture radar i
n
(SAR) and uses different signal waveforms
g
• Demonstrates successful applications of compressive sensing for

target detection and revealing building interiors
f
• Describes problems facing urban radar and highlights sparse o
reconstruction techniques applicable to urban environments r
• Deals with both stationary and moving indoor targets in the presence
U
of wall clutter and multipath exploitation
• Provides numerous supporting examples using real data and r
computational electromagnetic modeling b
a
Featuring 13 chapters written by leading researchers and experts,
n
Compressive Sensing for Urban Radar is a useful and authoritative

reference for radar engineers and defense contractors, as well as a R
seminal work for graduate students and academia.
a
d
a
r
K20856
EDITED BY
M o e n e s s A m i n

EDITED BY
M o e n e s s A m i n
Villanova University, Pennsylvania
Boca Raton London New York
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Contents
Preface................................................................................ vii
Editor.................................................................................. xi
Contributors........................................................................ xiii
1. CompressiveSensingFundamentals.......................................1
MichaelB.Wakin
2. OvercompleteDictionaryDesignforBuilding
FeatureExtraction.............................................................49
WimvanRossumandJaccodeWit
3. CompressiveSensingforRadarImagingof
UndergroundTargets.........................................................87
KyleR.Krueger,JamesH.McClellan,andWaymondR.Scott,Jr.
4. WallClutterMitigationsforCompressive
ImagingofBuildingInteriors ............................................ 123
FauziaAhmad
5. CompressiveSensingforUrbanMultipathExploitation............ 153
MichaelLeigsneringandAbdelhakM.Zoubir
6. MeasurementKernelDesignforHRRImagingof
UrbanObjects................................................................ 197
NathanA.Goodman,YujieGu,andJunhyeongBae
7. CompressiveSensingforMultipolarizationthrough-the-Wall
RadarImaging............................................................... 231
AbdesselamBouzerdoum,JackYang,andFokHingChiTivive
8. Sparsity-AwareHumanMotionIndication ............................ 251
MoenessG.Amin
9. Time–FrequencyAnalysisofMicro-DopplerSignalsBasedon
CompressiveSensing....................................................... 283
LjubišaStankovic´,SrdjanStankovic´,IrenaOrovic´,andYiminD.Zhang
v
vi Contents
10. UrbanTargetTrackingUsingSparse
Representations.............................................................. 327
PhaniChavaliandAryeNehorai
11. Three-DimensionalImagingofVehiclesfromSparseApertures
inUrbanEnvironment ..................................................... 361
EmreErtin
12. CompressiveSensingforMIMOUrbanRadar........................ 387
YaoYu,AthinaPetropulu,andRabinderN.Madan
13. CompressiveSensingMeetsNoiseRadar.............................. 429
MaheshC.Shastry,RamM.Narayanan,andMuralidharRangaswamy
Index ................................................................................ 461
Preface
I hope that the in-depth discussions and the scope of the topics covered in
this book benefit readers seeking to learn about the applicability and prac-
ticality of compressive sensing for urban radar. The book consists of 13
chapters and provides comprehensive coverage of the subtle issues in this
area. Itpresentssolutionstomanyproblemsthatarespecifictothesensing
ofopaquescenesandtargetswithobstructedlinesofsight.
Compressive sensing for urban radar is a hybrid between the two areas
of compressive sensing and urban sensing. In essence, it enables reliable
imaging of indoor targets using a very small percentage of the entire data
volume. This is the first book that focuses on the applications of com-
pressive sensing and sparse reconstructions to urban radar. The latter has
been a very active area of research and development. It involves localiza-
tion of targets inside enclosed structures and sensing of building interiors
using electromagnetic modality. Such capabilities are highly desirable for
lawenforcement,fireandrescue,emergencyrelief,andmilitaryoperations.
Problemsspecifictourbanradarsensingincludenear-field,complexmulti-
pathandrichscatterings,sloworzero-motionsoftargetsofinterest,exterior
wall electromagnetic wave blockage and strong signal reflections, shad-
owing and target obstructions, and detection and classification of human
motionbothfromabiometricandabiomechanicalaspect.
With the emergence of compressive sensing and sparse reconstruction,
the focus of urban radar has recently shifted toward relaxing constraints
on signal sampling schemes in time and space and on addressing logistic
difficultiesindataacquisition, especiallywhenusingground-based radars.
These challenges have traditionally hindered high-resolution imaging by
restricting both bandwidth and aperture and by imposing uniformity and
boundsontheirrespectivesamplingrates.
With a view toward providing timely actionable intelligence in urban
environments,ithasbecomeimportanttoexaminethecompressivesensing
paradigmanditsapplicabilitytothrough-the-wallradarimaging,giventhe
urbanenvironment–specificproblemsandchallenges.Recentresearchfind-
ings, supported by extensive analysis and experimentations, have revealed
performance gains when invoking the sparsity property of urban scenes.
Compressive sensing has enabled reduced cost, simplified hardware, and
efficientsensingoperationsofsparsebehind-the-wallscenes.Asaresult,we
arenowabletoreconstructsparsebuildinginteriorswithasmallfractionof
thefulldatavolumepreviouslyusedforback-projectionimagingandtarget
localization. Uniform and Nyquist sampled data are no longer a required
strategyfordataacquisition;noraretheyneededforalgorithmsimagingor
forsignalprocessing.
vii
viii Preface
Thisbookattemptstocapturetherecent, importantcontributionstothe
area of compressive sensing for urban radar. It consists of 13 chapters that
addressvariousaspectsandhavebeenwrittenbyleadersinthefield.Some
chapters cover stationary scenes, whereas others deal with moving targets.
Bothground-basedandairborneradarsareconsidered.Analysesandexam-
plesprovidedinthechaptersincludesteppedfrequencies,shorttimepulses,
andnoise-liketransmittedsignals, aswellasgeneratedradarreturnsusing
ray tracing–based simulations, electromagnetic modeling software, and lab
experimentations.
Chapter1providesanintroductiontocompressivesensingandpresents
thevariouscommonlyusedsparsereconstructiontechniquesthatareappli-
cable to urban radars. Chapter 2 addresses the linear forward model and
constructionofthedictionarymatrix, incorporatingmajorcanonicalindoor
signal reflectors such as walls and dihedrals. Chapter 3 deals with under-
ground objects using ground-penetrating radars and underscores the anal-
ogybetweenthrough-wallandthrough-groundcompressiveradarsensing.
Chapter 4 shows how exterior wall mitigation can be effectively achieved
with significantly reduced data and presents new techniques more robust
to random undersampled time-space and frequency-space radar signals.
Chapter 5 models reverberations and multipath clutter from exterior and
interior walls and exploits these models, within a sparse reconstruction
framework, to reduce ghosts and false alarms. Chapter 6 makes use of
prior knowledge on urban scenarios and uses Gaussian mixture models to
present information on a wide variety of indoor target objects, achieving
high-resolution imaging under sub-Nyquist sampling. Chapter 7 assumes
availability of co- and cross-polarization data and casts the urban radar
sensing problem as multiple measurement vectors in which there is a com-
mon dictionary and same scene supports for different polarimetric obser-
vations. Chapter 8 uses change detection and target velocity profiles to
induce sparsity and demonstrates successful indoor moving target indica-
tionsusingsparsereconstructionforbothsuddenandtranslationalmotions.
Chapter9dealswithnonstationaryradarreturnsthatresultfromshorttar-
getmotionsandincorporatescompressivesensingintojointtime-frequency
signal representations. Chapter 10 presents a sparsity-based algorithm for
multiple-targettrackinginatime-varyingmultipathenvironmentandcasts
theproblemofmultiple-targettrackingasablocksupportrecoveryproblem.
Chapter 11 discusses the three-dimensional wide angle synthetic aperture
radar (SAR) imaging of vehicles using airborne radar systems and uses
sparselysampledaperturesinlieuofadenselysampledsetofpointsinboth
azimuthandelevationangles,whichareassociatedwithtraditionalFourier
transform–based methods. Chapter 12 makes a case for compressive sens-
ing multiple-input multiple-output (MIMO) radars in urban settings and
demonstrates the advantages of transmitting multiple independent or cor-
relatedwaveformsutilizingthesparsityofthetargetreturnsinangle,range,
and Doppler space. Chapter 13 uses noise waveforms and demonstrates
Preface ix
how these signals and their system platforms are suitable for realizing the
promiseofcompressivesensinginradarimaging.
Ihopeyouenjoyreadingthisbookinitsentiretyorthechaptersofyour
interest.Iwouldliketothankallthecontributorsfortheirhardwork,excel-
lentdescriptionsoftheproblems, cleardelineationsoftheapproaches, and
inclusionofsupportivesimulationandexperiments.
MoenessG.Amin
(cid:2)
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