IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014 75
MIMO Radar for Target Detection and Localizationin Sensor Networks
Tariq Ali, Member, IEEE, Ahmed Z. Sadeque, Mohammad Saquib, Senior Member, IEEE, andMurtaza Ali, Senior Member, IEEE
Abstract—In this paper, we explore the use of multiple-input-multiple-output (MIMO) radar imaging systems in sensor net-works for the purpose of surveillance. We show how a MIMOradar imaging system can outperform a traditional radar systemby reducing the number of transceiver antennas without sac-rificing the image resolution. We use a minimum mean squareerror (MMSE) type receiver structure for this application thatminimizes both interference and hardware complexity. We showhow a group of sensor nodes can work together in determining oneor more targets’ locations. A radar image obtained from a singlenode can detect the presence of targets, but not provide a preciselocation information. However, spatially scattered group of nodescan together interpolate the targets’ positions relative to them andobtain localization information. We provide an in-depth analysis ofa MIMO radar system, and demonstrate the operation and utilityof such target localization methods using sensor networks throughsimulation.
Index Terms—Multiple-input-multiple-output (MIMO) radarimaging, sensor network, target detection and localization.
I. INTRODUCTION
THE NECESSITY of reliable yet practically imple-mentable surveillance systems has only grown over the
past few decades, and new technologies are always sought forto better serve the purpose. While optical imaging, traditionalradar systems, infra-red and laser technologies, etc. have alltheir own spheres of usage, there are certain scenarios wherea radar imaging system can better estimate a target’s presenceand/or position (e.g., in the dark, during inclement weather,places with heavy foliage, etc.). In this paper, we explore theuse of radar imaging systems in sensor networks for the purposeof surveillance. In that respect, we first show that a multiple-input-multiple-output (MIMO) radar system can outperformthe traditional radar technology by both improving resolutionand reducing system cost. We then demonstrate that a spatiallyscattered group of sensor nodes with radar imaging capabilitycan together determine the targets’ positions effectively.
Manuscript received May 20, 2011; revised November 17, 2012 andDecember 21, 2012; accepted January 16, 2013. Date of publication June 27,2013; date of current version February 5, 2014. This research was supported inpart by the Semiconductor Research Corporation (SRC) and the Texas AnalogCenter of Excellence (TxACE) under Task Order 1836.101.
T. Ali and M. Saquib are with the University of Texas at Dallas, Richardson,TX 75080 USA (e-mail: [email protected]; [email protected]).
A. Z. Sadeque is with Qualcomm Technologies, San Diego, CA 92121 USA(e-mail: [email protected]).
M. Ali is with the Systems and Applications R&D Center, Texas Instruments,Dallas, TX 75266-0199 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/JSYST.2013.2260632
Fig. 1. Virtual array of MIMO radar. Here, M = 3, N = 4 and MF denotesmatched filter.
Currently traditional radar technology is used in radarimaging systems that operate with large number of anten-nas/transceivers [1]. The aperture length, which in turn de-termines the cross range resolution of an image at a givenfrequency, depends on the length of the effective receivedantenna array [2]. Radar imaging systems are able to deliverhigh resolution images as they operate within a high frequencyrange. Therefore, further improvement of image resolution in aradar imaging system entirely depends on the aperture length.Recent study suggests that MIMO radar technology enhancesthe length of the effective aperture through the concept ofvirtual array [3]–[6]. MIMO radar is an emerging technologythat transmits orthogonal probing signals via multiple transmitantennas simultaneously. For a MIMO radar system with Mtransmit and N receive antennas, placed at a spacing of dTand dR, respectively, dT = NdR yields a virtual array of NMreceive elements which determines the aperture length; seeFig. 1. This increased virtual aperture results in many MIMOradar advantages such as higher image resolution, improved pa-rameter identifiability, and better accuracy for ground movingtarget indication (GMTI), as compared to a standard phased-array radar [4]–[18]. In this paper, we exploit the improvementof image resolution due to the enhanced virtual array of MIMOradar systems.
When radar is employed for imaging purpose, the goal isto estimate the radar cross section (RCS) of targets precisely.Since the cross range resolution of an image at a given
76 IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014
frequency depends on the length of the received antenna array,for a given number of antennas, a MIMO radar system is ex-pected to outperform the traditional radar system (that operateswith M = 1) in terms of cross range resolution. Therefore,a resolution similar to the traditional system can be achievedusing a fewer number of antennas. Reduction of antennas sim-plifies the system’s analog and RF chain by reducing hardwarecomponents which results in less numerical errors. In addition,MIMO transmission makes the radar transmitter more efficientin managing the nonlinearity issue of the power amplifier [19],[20]. Therefore, reduction of antennas decreases the number ofhardware components which reduces the cost of radar imagingsystems. It is well known that MIMO system suffers frominterference due to parallel transmission of the probing signalswhich demands appropriate receiver to suppress the interfer-ence. Therefore, in this work, we employ a minimum meansquare error (MMSE)-like receiver that handles the interferenceissue. Further discussion on the choice of the receiver will beprovided in Section II-B.
Radar imaging systems [21]–[23] are currently deployed forenvironmental monitoring, security surveillance, earth-resourcemapping, target detection, and various military applications[24]. Unlike optical imaging, radar imaging is capable of work-ing in day or night as well as in inclement weather. Radar signalcan penetrate the optically opaque materials such as foliage,buildings, soil or human tissue and therefore, this imaging sys-tem is a better choice for security and surveillance over opticalimaging. Currently, the use of radar technology is limited tomilitary or other large scale applications due to its high cost.However, low cost radar imaging systems have a potentialfor deployment in civilian applications such as small-scalepersonnel surveillance and sensor network target localization.One of the objectives of our research is to design low cost radarimaging systems for target detection and localization in sensornetworks. Therefore, we employ a MIMO radar to achievean image resolution identical to the traditional one but withless number of antennas. Our numerical results demonstratethat depending on the system configuration, MIMO radar withMMSE-like receiver is capable of providing the same imageresolution as the traditional one but with almost 30% lessantennas.
In this paper, vectors and matrices are denoted by boldfaceuppercase and lowercase letters, respectively. The superscripts†, ∗ and � denote the Hermitian, conjugate and transposeoperations on matrices, respectively. The rest of this paper isorganized as follows. In Section II, we describe the operationof a MIMO radar system and our proposed receiver, alongwith some illustrative results. In Section III, we show how aradar imaging system can be utilized in sensor networks fortarget detection and localization. We provide some numericalresults in Section IV, and finally draw the conclusions inSection V.
II. MIMO RADAR IMAGING SYSTEM
In this section, we describe the MIMO radar system model,the estimation technique and illustrate a few examples of the
Fig. 2. MIMO radar imaging system.
resultant images that are obtained before proposing the targetlocalization scheme in the next section.
A. System Model
As depicted in Section I, the cross range resolution of animage at a given frequency depends on the length of thereceived antenna array. For a given number of antennas, aMIMO radar system is expected to outperform the traditionalradar system. The distance between two consecutive receiveantennas is usually maintained as half the wavelength of thecarrier. We model our imaging system in the way describedin [5], [6]. Fig. 2 shows an illustration of the transmitter andreceiver orientation with respect to the target. The MIMO radaris equipped with M transmitters and N receivers where the mth
transmit antenna transmits a waveform xm of length L
xm = [xm(1) · · · xm(L)]� ; m = 1, . . . ,M. (1)
The waveform matrix X can be described as
X = [x1 x2 · · · xM ] . (2)
The target scene is divided into P range bins and K anglebins. The transmitted waveform matrix is appended with zerosand expressed as
X̃ =
[X
0(P−1)×M
](3)
where 0(P−1)×M is a (P − 1)×M matrix of zeros and P − 1denotes the maximum delay between the various range bins andthe closest range bin. The reflected signal D† can be expressedas [5], [6]
D† =P∑
p=1
K∑k=1
αp,kakb�k X̃
†Jp +E† (4)
where αp,k is the scattering or reflection co-efficient and E isthe additive white noise. Here, ak and bk denote the receive
ALI et al.: MIMO RADAR FOR TARGET DETECTION AND LOCALIZATION IN SENSOR NETWORKS 77
and transmit steering vectors, respectively, and Jp is a (L+P − 1)× (L+ P − 1) shift matrix that can be written as
Jp =
⎡⎢⎢⎢⎢⎢⎢⎣
p︷ ︸︸ ︷0 . . . 0 1 0 . . . 00 . . . 0 0 1 . . . 0
. . .1
0 0
⎤⎥⎥⎥⎥⎥⎥⎦. (5)
We can rearrange (4) to simplify the expression of receivedsignal using the vector operation of matrix. Let us define
d =vec[DH ] (6)
yp,k =vec[akb
�k X̃
HJp
](7)
e =vec[EH ] (8)
as column vectors originating from the terms in (4). The matrix
Y = [y1,1 · · · y1,K y2,1 · · · yP,K ] (9)
contains the vectors, yp,k for all values of p and k, and thevector
α = [α1,1 · · · α1,K α2,1 · · · αP,K ]� (10)
contains the reflection coefficients for all bins. Hence, we canexpress the received signal as
d = Yα+ e. (11)
Our goal is to estimate the value of αp,k from the aboveequation, as discussed next.
B. Proposed Algorithms
It is well known that MIMO system suffers from interferencedue to parallel transmission of the probing signals. Therefore,appropriate receiver design is significant for MIMO radar imag-ing systems. Several receivers have already been proposed inthis field. The most common technique used in radar imagingsystems is the delay-and-sum (DAS) (or matched filtering)technique. However, low image resolution and high sidelobelevels refrain this method from employment in MIMO radarimaging systems. Adaptive beamforming techniques such asCapon [25], amplitude and phase estimation (APES) [26] etc.also fail to work when probing signals are highly correlatedand the number of snapshots are low [6]. The iterative adaptivetechnique (IAA) works well in single snapshot scenarios [6] butrequires the noise power to be updated in each iteration. In thispaper, the reflection co-efficients are estimated as [27]
α̂ = {Y†Y + γI}−1Y†d (12)
using an MMSE-like receiver, where γ is a tuning parameterused for regularization.
The signal processing circuit of the MIMO receiver needs tohave computational accuracy and low latency because of therequirement of real-time operability. Matrix inversion is a keycomponent in MMSE implementations. There are an abundant
of VLSI architectures for matrix inversion [28]–[30] based onGauss-Jordan Elimination, LU factorization, Cholesky Factor-ization, Matrix Inversion Lemma, QR decomposition, ModifiedGram-Schmidt, etc.[31], [32]. The accuracy and speed of ma-trix inversions provided by these architectures make it viablefor the MMSE receiver to be employed in the context of thispaper.
C. Illustration of Resultant Images
A few illustrations of the resultant images obtained usinga MIMO radar imager, which is to be accommodated at asingle sensor network node, are portrayed in this section. Fig. 3shows how a point target is captured by a MIMO radar im-agery. Fig. 3(a) shows the high reflectivity (denoted as α) ofa point target located near the middle of the radar imager’sview, whereas the rest of the pixels represent the reflectivityof the surrounding objects (e.g., trees) with lower reflectivity.Fig. 3(b) shows the resultant radar image obtained using ourproposed scheme. When the average reflectivity of the vicinityis higher, we obtain Fig. 3(c), which shows a slightly degradedradar image. While both Fig. 3(b) and (c) were obtained usinga signal-to-noise ratio (SNR) of 15 dB, the effect of a lowerSNR (5 dB) can be observed in Fig. 3(d). In Fig. 3(b)–(d), weemploy a MIMO radar system with M = 3 and N = 8 which iscapable of providing better image resolution than the traditionalradar with M = 1, N = 15. For visual illustration, the resultantimages with M = 1, N = 15 (which is the traditional single-input multiple-output (SIMO) radar), and with M = 2, N = 5are shown in Fig. 3(e) and (f), respectively. The MIMO radarwith 11 (M = 3 and N = 8) antennas [Fig. 3(b)] yields animage almost identical to that with 16 (M = 1, N = 15) an-tennas [Fig. 3(e)], but a further reduction in the number ofantennas to 7 (M = 2, N = 5) causes the image to deteriorate[Fig. 3(f)].
The image obtained using the MIMO radar in a particularsensor node can detect a target, but cannot provide any specificlocalization information. In the next section, we show how acombination of sensor nodes each equipped with MIMO radarimaging capability can be efficiently utilized in finding theposition information of one or more targets as well.
III. TARGET LOCALIZATION USING SENSOR NETWORK
In this section, we illustrate how a sensor network endowedwith radar imaging capability can be used in target detectionand localization. Each node in a sensor network has a MIMOradar installed in it, and at a given instance the radar can takean image that spans a certain angle of view. For example, if aradar can capture a view of 30◦, it will take it 12 snapshots tohave a 360 degree look around. When a certain area of terrainunder surveillance is to be watched, we will employ multiplesensor nodes facing at the area of interest and combine the datareceived from their images. As illustrated in Fig. 3, an imagefrom a single node can only indicate the presence of a target atbest, but not provide any position information. However, sincethe location and angle orientation of the radar is known, we canestimate a line (whether narrow or thick) over which the target
78 IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014
is possibly lying. When he have multiple images from differentsensors at different locations, we can find the intersection ofthese lines and obtain a good estimate of the location of thetarget.
If the number of sensor nodes covering the area of interestat a given time is Nnodes, we will have Nnodes images thatwere taken from different locations. Let the angular coverageof each node be φ, and their orientation be βi. The ith nodewill have a cone-shaped coverage region within [βi − φ/2, βi +φ/2] range originating from its location. The intersecting areaof these cone-shaped regions from the Nnodes nodes will be theregion under surveillance. Since a point target can be mobile,these images need to be taken in quick succession. While themultiple antennas within a single node transmit their signalssimultaneously (which is the key concept of MIMO systems),the different nodes may or may not operate at the same time.If they do, then the spreading codes that they use will need tobe long enough to avoid inter-node interference. However, theprobable speed of the targets present in the vicinity are usuallynegligible compared to the speed of the successive imaging.
Once a certain area under surveillance is covered, the radarswill change their orientation and together can focus on adifferent region and start the whole process again. The imagesobtained in different nodes are then sent to a central processingunit (which may be co-located within one of the nodes) andthe lines of possible target occupancy are super-positioned toextract the target locations. The details of this method willbe more evident in the next section where we show somesimulation results employing this technique.
IV. NUMERICAL RESULTS
In this section, we provide simulation results for our pro-posed method. In our simulation, we divide the target area into24 range and 61 angle bins. The SNR is set to 20 dB. As forthe transmit waveforms, we use Hadamard codes scrambledwith PN sequences similar to the IS-95 Code Division MultipleAccess (CDMA) systems as done in [5], [6]. The length of thewaveform is L = 256 samples, which are padded by P − 1 =23 zeros. It is well known that the Hadamard code is generatedas a power of 2 and therefore, the waveform generation tech-nique for the traditional and MIMO system is straightforward.The separation between the consecutive receive antennas isdr = 0.5λ and the transmitters are set an Ndr spacing apart.
It is customary to use additive white Gaussian noise (AWGN)channels in MIMO radar analysis [5], [33] since the MIMOoperation is particularly advantageous in multipath situationsbecause of the spatial diversity that it offers. It can be in-ferred from (4) that the received signal contains the originalwaveform as well as shifted versions of it, which evolve fromdifferent range bins of the target scenario. The overlapping ofthese shifted signals resembles the multipath related problemsencountered in traditional wireless communication systems. Ina communication system, MIMO architectures are extensivelyused to improve the bit error rate in multipath systems. How-ever, the tradeoff between target SNR and signal-to-clutter-plus-noise ratio (SCNR) in multipath propagation environmentsis a crucial consideration in MIMO radar. There have beenseveral research efforts in this field. In [34], a transmit-receive
ALI et al.: MIMO RADAR FOR TARGET DETECTION AND LOCALIZATION IN SENSOR NETWORKS 79
Fig. 4. Illustration of the sensor network topology and the respective radar images.
directionality spectrum (TRDS) is used to examine the cluttercharacteristics at a range-Doppler bin of interest, most notablyin multipath situations. A tracking algorithm is proposed in[35] to optimize multipath returns by dynamically selectingthe parameters of the transmitted waveform to minimize thetracking mean square error (MSE). The authors of this paper arealso working on combining MIMO operation with space-timediversity techniques [36] to further reduce multipath effects.Nevertheless, the purpose of this paper is to show the utilityof the localization algorithm we proposed, and we demonstratethat using an AWGN channel. The performance is expectedto vary to some degree when the channel is different, butas depicted, any such deviation can be compensated with theproper waveform and receiver design.
The target localization method is illustrated next through athorough example. Fig. 4 shows a sensor network with threenodes. Their angles of orientation are shown, and together theycover the circle shown in the middle. Three point targets (A,B and C) are also shown in the figure. While Fig. 4 is a top-view of the network topology, the images captured by eachof the sensors will be 2-D in the vertical plain. These imagesare shown in the figure. Based on the location (the angularbin) of the point targets in the image obtained at Sensor-1,it can estimate that the targets may lie on the dark shadedregions shown in Fig. 5(a). Sensor-1 has no information ofthe location of the targets with respect to the topology, butcan only give an indication of their directions with respect toitself. Note that the sub-figures in Fig. 5 are all top-views of thearea under surveillance. Similarly, Fig. 5(b) and (c) show the
tentative directions of the point targets. When these estimatesare combined, we obtain Fig. 5(d), which is the intersectionof the regions in Fig. 5(a), (b) and (c). As can be seen inthis figure, all three of the point targets have been detectedand quite closely localized (compare with the original targetlocations in Fig. 4). However, this method also may providesome false alarms (points D and E) regarding target locations.From the given set of images, there may be some additionalintersection areas depending on the locations of the sensors,the reflectivity of the vicinity, and the ambient noise in theenvironment. Note that if there is a sensor in the middle thattakes several successive images by changing its orientation,this ambiguity can be removed. The images taken at each nodecontain the target pixels in the same range bins for the givenexample. If the distances from the targets to a given sensorvaries widely, this may not be the case, but a similar approachcan be taken by interpolating the tentative directions for eachof the range bins containing the target. In that case, the rangeinformation will narrow down the targets’ location possibilitiesand help resolve any additional ambiguity that is associated.
In Table I, we show how the number and location of thesensors affect the performance of the system. When there areonly two sensors available, the best orientation is to place themwith perpendicular lines of view. It yields a high number of falsepositives due to the superposition of two sets of projected linesfor every target present. With three or more sensors, they can beeither arranged over a circle around the area under surveillanceat equal angular increments (i.e., with lines of view that are360◦/Nnodes degrees apart, where Nnodes is the number of
80 IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014
Fig. 5. Possible target location (a) based on image from Sensor-1, (b) based on image from Sensor-2, (c) based on image from Sensor-3, and (d) based on imagesfrom all three sensors.
TABLE IEFFECT OF VARIATION IN NUMBER AND LOCATION OF SENSORS
sensors), or one or more can be placed in the middle of thearea. Note that, in the latter case, the imager needs to capturemultiple radar images of the area surrounding it. If it is feasibleto permit the additional time required for this rotation, then itcan improve the radar performance as demonstrated in Table I.Also, the performance, in general, starts improving with an
increasing number of sensors, as expected. Therefore, furtherstudy of the impact of the relative locations of the sensor nodeson the false alarm rate remains as our future work. Also, theeffect of SNR, the reflection coefficients of the vicinity, etc. aresome more crucial studies that are to be performed in the nearfuture.
In this paper, a negligible relative speed is assumed betweenthe radar and the targets and hence, the Doppler effect is ig-nored. The sensor network localization problem is one that aimsat finding the locations of target objects and network nodes in arelatively large area, where the objects of interest are usuallyeither bulk objects that are static for a long time, or slowlymoving targets. With regard to the speed of the MIMO radarimaging process, these speeds are negligibly low and hence, inmost cases will not affect the system performance considerably.However, if the target velocities are higher, the Doppler effectwill cause range ambiguity. Since we are exploiting mainlythe angular data (from bins) in our algorithm, these effectswill likely be small. More studies on Doppler effects, and alsomultipath channels as described above, remain as our futurework.
ALI et al.: MIMO RADAR FOR TARGET DETECTION AND LOCALIZATION IN SENSOR NETWORKS 81
V. CONCLUSION
In this paper, we propose the use MIMO radar imagingsystems in sensor networks for the purpose of surveillance.The superiority of the MIMO radar imaging system over thetraditional radar system is shown, and a novel MMSE-likereceiver structure is used that minimizes both interference andhardware complexity. Next we show how a group of sensornodes can work together in determining one or more targets’locations. While a radar image obtained from a single nodecan only detect the presence of targets, a spatially scatteredgroup of nodes can together interpolate the targets’ positionsrelative to them and obtain localization information. Simi-lar interpolation methods in the context of optical imaging,specifically using stereo vision, have been developed [37]–[39] that often use various illumination techniques. However,as stated earlier, their efficacy is compromised in the presenceof optically opaque materials. In our future studies, we willleverage from the adaptation of suitable stereo vision tech-niques, and also exploit the range information from the radarin addition to the angular information. In this paper, we havedemonstrated the operation and utility of MIMO radar targetlocalization methods in sensor networks through simulation,and provided an in-depth analysis of the MIMO radar imagingsystems.
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82 IEEE SYSTEMS JOURNAL, VOL. 8, NO. 1, MARCH 2014
Tariq Ali (S’08–M’11) received the B.Sc. degree inelectrical engineering from Bangladesh University ofEngineering and Technology, Dhaka, Bangladesh, in2001, the M.S. degree in electrical engineering fromUniversity of Texas at Arlington, Arlington, TX, in2004, and the Ph.D. degree from University of Texasat Dallas, Richardson, TX, in 2011, where he worksas a Post-Doctoral Research Associate and Lecturer.
His research interests include convergence of het-erogeneous wireless networks, interference manage-ment, radar imaging, sensor network localization,
etc. He has worked on several collaboration projects with Nokia Corporation,SNRLabs Corporation, US Air-force, Texas Instruments, and SemiconductorResearch Corporation.
Ahmed Z. Sadeque received the B.Sc. degree inelectrical engineering from Bangladesh University ofEngineering and Technology, Dhaka, Bangladesh, in2002, the M.S. degree in electrical engineering fromThe University of South Alabama, Mobile, AL, in2006, and the Ph.D. degree in electrical engineeringfrom The University of Texas at Dallas, Richardson,TX, in 2012.
Currently he is with Qualcomm Technologies Inc.,San Diego, CA, where he works on Long TermEvolution (LTE) technologies. His research interests
include orthogonal frequency division multiplexing (OFDM), multiple-inputmultiple output (MIMO) systems in communications and radar, signal process-ing techniques for radar, and millimeter-wave imaging.
Mohammad Saquib (SM’09) received the B.Sc.degree in electrical and electronics engineering fromBangladesh University of Engineering and Tech-nology, Dhaka, Bangladesh, in 1991, and the M.S.and the Ph.D. degrees in electrical engineering fromRutgers University, Newark, NJ, in 1995 and 1998,respectively.
Prior to joining University of Texas at Dallas,Richardson, TX, in 2000, where he is presently anAssociate Professor in the Electrical EngineeringDepartment, he worked at MIT Lincoln Laboratory,
Lexington, MA, USA, as a member of the Technical Staff, and at LouisianaState University, Baton Rouge, LA, USA, as an Assistant Professor. Hisresearch interests include various aspects of wireless data transmission, radioresource management, and signal processing techniques for radar applications.
Murtaza Ali (SM’08) received the B.Sc. fromBangladesh University of Engineering and Technol-ogy, Dhaka, Bangladesh in 1989, and the M.S. andPh.D. from University of Minnesota, Minneapolis,MN, in 1993 and 1995, respectively.
He is a Distinguished Member of Technical Staffat Texas Instruments (TI), Dallas, TX. He leads theHigh Performance Signal Processing ApplicationsR&D activity in the Systems and Applications R&DCenter. His work is focused on novel applications ofTI’s embedded multi-core digital signal processors.
His activities include linear algebra, radar, seismic and medical imaging. In thepast, he led R&D teams for mobile WiMAX, ADSL and voice-band modemtechnologies in TI.
