Novel System Architecture and Waveform Design for

3630 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 61, NO. 8, OCTOBER 2012

Novel System Architecture and Waveform Design forCognitive Radar Radio Networks

Yogesh Nijsure, Yifan Chen, Member, IEEE, Said Boussakta, Senior Member, IEEE, Chau Yuen, Member, IEEE,Yong Huat Chew, Member, IEEE, and Zhiguo Ding, Member, IEEE

Abstract—A novel approach to combining communication andradar functionalities in a single waveform design for cognitiveradar radio (CRR) networks is proposed. This approach aimsat extracting the target parameters from the radar scene, aswell as facilitating high-data-rate communications between CRRnodes, by adopting a single waveform optimization solution. Thesystem design technique addresses the coexisting communicationand radar detection problems in mission-critical services, wherethere is a need of integrating the knowledge about the targetscene gained from distinct radar entities functioning in tandemwith each other. High spatial resolution and immunity to multi-path fading make ultrawideband (UWB) signals an appropriatechoice for such applications. The proposed solution is achievedby applying the mutual-information-based strategy to design thesequence of UWB transmission pulses and embed into them thecommunication data with the pulse position modulation scheme.With subsequent iterations of the algorithm, simulation resultsdemonstrate an improvement in extraction of the parameters fromthe radar scene, such as target position and impulse response,while still maintaining high-throughput radio links with low biterror rates between CRR nodes.

Index Terms—Cognitive radar radio (CRR) network, jointcommunication–radar waveform design, mutual information(MI), target signature extraction, ultrawideband (UWB).

I. INTRODUCTION

A. Background on Cognitive Radar Waveform Design

COGNITIVE radar is an innovative paradigm to de-scribe radar systems that constantly employ information-

gathering mechanisms to adapt their operational modesand facilitate intelligent illumination of the environment.Subsequently, updated information about the radar scene canbe utilized to allocate crucial resources like transmission power

Manuscript received November 28, 2011; revised March 17, 2012 andApril 22, 2012; accepted May 30, 2012. Date of publication June 6, 2012;date of current version October 12, 2012. This work was supported in partby the UK Engineering and Physical Sciences Research Council under GrantEP/G069042/1 and in part by the International Design Center under GrantIDG31100102 and Grant IDD11100101. The review of this paper was coor-dinated by Prof. H.-H. Chen.

Y. Nijsure, Y. Chen, S. Boussakta, and Z. Ding are with the School of Elec-trical and Electronic Engineering, Newcastle University, NE1 7RU Newcastleupon Tyne, U.K. (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).

C. Yuen is with the Singapore University of Technology and Design,Singapore 138682 (e-mail: [email protected]).

Y. H. Chew is with the Institute for Infocomm Research, Singapore 138632(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/TVT.2012.2203328

and spectrum in a more efficient manner [1], [2]. This infor-mation is relayed to the transmitter by the receiver through acontinuous feedback loop, which allows the development ofwaveform design techniques that offer better target resolutioncapabilities [3]. Excitation pulses can be optimized by applica-tion of information theory to radar signal processing. Bell [4]studied the design of waveforms in the context of illumina-tion of extended objects for target detection and informationextraction. Yang and Blum [5] extended the work of Bell [4] byusing mutual information (MI) as the waveform optimizationcriterion subject to limited transmission power in the multiple-input–multiple-output radar configuration.

The work in [5], in particular, focuses upon the problemof radar waveform design for target classification and iden-tification, where the conditional MI between the random tar-get impulse response and the reflected signals is maximizedgiven the knowledge of the transmitted signals. An alternativeapproach presented in [6] is based on minimization of meansquare error in estimating the target response. The analysis in[6] indicates that the two aforementioned problems lead to thesame waveform solution. Our previous work [3] focuses upondesigning ultrawideband (UWB) transmission signals aimedat minimizing the MI between the received radar pulses atsuccessive time instants. This is achieved by selecting theprobing signals that result in independent responses from theradar scene in a bid to gain more knowledge about the changingtarget parameters at each time instant.

In this paper, we seek to develop a novel cognitive ar-chitecture for the joint communication–radar waveform de-sign. We first review some of the existing works on jointcommunication–radar networks in the following section.

B. Background on Joint Communication–Radar Systems

Wireless communication and radar technologies have al-ways been independent research entities. On the one hand, theformer focuses upon achieving the best possible informationcapacity across a noisy wireless channel under power andcomplexity constraints. On the other hand, the latter attemptsto achieve better target resolution and parameter estimationin the presence of surrounding clutter and noise. In recentyears, research into integrating communication and radar un-der a common platform has gained significant momentum[7]–[9]. Such a joint system would constitute a unique cost-efficient solution for future intelligent surveillance applications,where both environmental sensing and establishment of adhoc communication links are essential. These types of systems

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NIJSURE et al.: SYSTEM ARCHITECTURE AND WAVEFORM DESIGN FOR COGNITIVE RADAR RADIO NETWORKS 3631

can be deployed in mission-critical services such as militaryoperations to address surveillance and communication is-sues simultaneously [7]. It is thus envisioned that futurepersonal communication devices will have comprehensiveradar-like functions such as spectrum sensing and localiza-tion, in addition to multimode and multiband communicationcapabilities.

Recent contributions such as [7] and [8] in particular focusupon the development of devices that have multiple radio func-tions and combine communication and radar in a small portableform with ultralow power consumption. These works haveadopted orthogonal frequency-division-multiplexing (OFDM)techniques fused with UWB technologies to realize thecommunication–radar integration. However, these designs cre-ate other implementation issues, such as excessive demand ofsignal processing power, high-speed analog-to-digital circuitry,and agile radio frequency front end for multimode operation.Furthermore, systems employing UWB-OFDM for localization[7]–[9] utilize the same waveform family for designing jointcommunication–radar signals. Consequently, these methodsshare a common drawback due to the fact that the autocor-relation (related to the range resolution of radar) of UWB-OFDM signals depends on both the location of the notch andthe OFDM signal bandwidth. Hence, although the radar targetrange estimation is unaffected by the presence of an OFDMsignal, its range resolution depends on the notch bandwidth intowhich the OFDM signal is embedded.

C. Joint Communication–Radar Waveform Design From aCognitive Radar Radio (CRR) Perspective

In the current work, we combine the cognitive waveformselection algorithm presented in [3] and the UWB pulse posi-tion modulation (PPM) technique to obtain a unified waveformdesign solution, which offers superior radar performance andhigh-data-rate communication capability between CRR nodes.With this method, the radio and radar signals can coexist bysharing the same frequency band. Hence, the range resolutionof the radar module is not affected by the communicationsignal design parameters. This makes the proposed UWB-PPM method superior to the existing UWB-OFDM solutions.The CRR waveforms obtained would not only benefit froman information-theoretic approach for efficient target parameterextraction but would also utilize the same signal for establish-ing ad hoc communication links by adopting the UWB-PPMtransmission strategy.

We consider the IEEE 802.15.4 MAC layer for the CRRnetwork and adopt the relevant multipath indoor channel modelproposed in [10] with the existence of a line-of-sight (LOS)path. In the case of the radar link, the LOS component cor-responds to the direct target echo, whose radar cross-section(RCS) scintillation is characterized by the Swerling III model[11]. Each CRR node is assumed to be a monostatic radar unit,where the transmitter and receiver subsystems are collocatedand hence can share information about the target parametersbetween themselves. We also assume perfect synchronizationbetween CRR nodes to eliminate the multiple access interfer-ence between different users and leave the more complicated

asynchronous operation scenario for future investigation. Inaddition, we have chosen UWB-PPM instead of continuouswaveforms for the following reasons:

1) The operational environment could be harsh to radiocommunications caused by densely populated scatterers.UWB signals are relatively immune to multipath channelfading in this case.

2) The UWB-PPM waveform design is robust to hostile en-vironments by providing low probability of interceptionand avoiding jamming interference.

3) The UWB-PPM waveform solution enables rapid low-transmission-power ad hoc links, which can be config-ured “on the fly” without reservation or contention ofavailable spectra.

The foregoing benefits offered by UWB-PPM make it suit-able for the current system design problem. The key contribu-tions of this paper can be summarized as follows:

1) We develop a novel cognitive radar probing strategybased on the concept of MI minimization between suc-cessive backscatter pulses for the extraction of targetparameters, such as its relative distance from a CRR node,and its impulse response and velocity.

2) We propose an original UWB-PPM-based jointcommunication–radar waveform design scheme.

3) We provide performance analysis of the CRR network interms of the target parameter extraction and communica-tion bit error rate (BER) between CRR nodes.

The rest of this paper is organized as follows. In Section II,we present the system architecture for the CRR network underconsideration. In Section III, we analyze the performance ofthe communication link achieved through the CRR waveform.In Section IV, we describe the MI-based cognitive waveformselection approach. In Section V, we present simulation resultsfor target impulse response extraction, waveform optimization,and BER as compared to other related works. Finally, someconcluding remarks are drawn in Section VI. Throughout thispaper, we use | · | to represent the determinant of a matrix, (·)Tto indicate the Hermitian transpose, and E(·) to represent theexpectation operator.

II. SYSTEM ARCHITECTURE

As extensively discussed in the existing literature, modernradar systems make use of pulse compression techniques suchas linear frequency modulation or phase-coded waveforms em-ploying Barker codes or Costas codes to improve the targetdelay Doppler resolution [12]. In this paper, we adopt theidea of utilizing phase-coded waveforms for the generation oforthogonal sequences required for transmission over varioustransmit antennas. We will consider UWB probing signals [2],although the general methodology is also applicable to anyother type of excitation. The waveform comprises a sequence ofUWB Gaussian monocycles in which the phase of each pulse ismodulated in accordance with the orthogonal sequences corre-sponding to the column vectors of a specific Walsh–Hadamard


Fig. 1. (a) Coexistence of communication and radar functionalities in a CRR network. (b) CRR node architecture.

matrix [12]. Each normalized Gaussian monocycle takes thefollowing form:

u(t) =

[1 − 4π

(t

T

)2]exp

{−2π

(t

T

)2}

(1)

where T determines the pulse width and is assumed to be0.2 ns, which is a value commonly used in UWB rangingapplications [12].

A. CRR Network Setup

Fig. 1(a) exemplifies a typical CRR network comprisingdistinct nodes capable of maintaining communication linksbetween themselves, gathering data on the radar scene from

sensors (suppose that targets also induce certain events ob-servable at the sensors), and maintaining active probing ofthe target scene through the backscatter radar signals. Thecommunication and radar functionalities occur simultaneously,where the same CRR signal is used for both communicationsbetween network nodes and target impulse response estimation.For simplicity, we assume perfect synchronization betweenCRR nodes, as mentioned in Section I-C. The CRR units arerequired to intelligently select the most effective surveillancemechanisms while maintaining the ability to communicate crit-ical information between themselves via an ad hoc network toshare the acquired radar scene information. These nodes canalso gather intelligence on a specific phenomenon of interestlike radioactivity or a biohazard induced by the targets through


Fig. 2. (a) CRR transmission waveform (16-bit) obtained from the column vector of the Walsh–Hadamard code matrix and the PPM data. (b) Orthogonality ofCRR waveforms. (c) Spectrum of the received CRR signal at a reference distance of 20 m at 4-GHz center frequency.

communicating with remote sensors. Fig. 1(a) represents such asetup in which this joint communication–radar operation couldbe realized.

B. CRR Node Transmitter Subsystem

Fig. 1(b) presents the internal architecture of a singleCRR node. We initially construct an ensemble of orthog-onal sequences of UWB Gaussian monocycles based uponthe Walsh–Hadamard codes. Each sequence corresponds to aparticular column vector of the Walsh–Hadamard matrix asillustrated in Fig. 2(a). Next, all the column vectors of theensemble matrix undergo PPM in accordance with the com-munication data to be sent over the CRR link, as shown inFig. 2(a). The data link could be established between two CRRnodes or between a CRR node and a remote sensor monitoringthe phenomenon of interest. To facilitate identification, theunique addresses of each pair of source and destination areembedded in the preamble of the data to be sent. A UWB-

PPM signal is selected by the waveform selection module fromthe ensemble based on the MI minimization algorithm to bedescribed in Section IV. This integrated signal is then sent bythe transmitter to probe the radar environment and send the datato other CRR nodes. The received signal comprises either thetarget return or the communication data from other nodes andsensors. Subsequently, the former can be used to estimate targetparameters like range, velocity, and impulse response.

C. Target Channel Model

We now consider a CRR node with the same antenna usedfor both transmission and reception purposes. Let x representa particular sequence of orthogonal waveforms to be usedfor transmission. Let n represent the additive white Gaussiannoise (AWGN). As verified by our simulation results to bediscussed in Section III, PPM does not affect the orthogonalitybetween various CRR waveforms. Thus, we can safely designand optimize radar excitations with or without PPM.


We can express the received signal at a CRR node as

y = Hx+ n. (2)

The variable x ∈ CK×1 denotes the transmitted waveform vec-

tor with K being the length of the UWB radar sequence, y ∈C

K×1 denotes the received signal vector, H ∈ CK×K denotes

the target channel impulse response comprising the two-waylink of the transmitter-to-target and target-to-receiver segments,and n ∈ C

K×1 denotes the noise vector. C represents the com-plex number domain. We further define E(HTH) = RH to bethe target channel covariance matrix and E(nTn) = Rn to bethe noise variance.

RCS scintillation of the target can vary slowly or rapidly,depending on the target’s size, shape, dynamics, and relativemotion with respect to the radar. Thus, due to the wide varietyof RCS scenarios, its scintillation is modeled statistically as arandom process. Depending upon the motion and clutter char-acteristics, target responses have been classified into Swerlingmodels, as described in [11]. In Swerling III, the RCS samplesmeasured by the radar are correlated throughout an entire scanbut are uncorrelated from scan to scan (slow fluctuation), andthe target scene is dominated by a single powerful scatteringcenter with many weak reflectors in its vicinity. This model willbe considered here. More specifically, the power of each entryof H in (2) is governed by the Swerling III variation (see also[13] and [11])

f(ξ) =1ξav

exp

(− ξ

ξav

)(3)

where ξ > 0 represents the random RCS fluctuation with ξavbeing the average RCS. This model can be extended to accom-modate other nontarget clutter sources in addition to the desiredtarget.

D. CRR Node Receiver Subsystem

The received signal is passed on to a matched filter bankas illustrated in Fig. 1(b), which matches the signal to eachindividual transmission waveform stored in the receiver. At thisstage, the radio signal that exists in the form of the UWB-PPM data is extracted by removing the excess delays betweenUWB pulses through demodulation. On the other hand, theradar signal is sent to the target impulse response and parameterestimation module for discrimination of the target from thesurrounding clutter.

The estimated channel response and received signal char-acteristics, such as noise variance, are forwarded to the MIminimization module. In light of the updated radar scene, theMI minimization module selects a suitable sequence for thetransmit antenna to acquire the best knowledge about the targetin the next time instant. This operation facilitates adaptiveillumination of the radar environment and leads essentially toa cognitive dynamic system featuring the following two prop-erties described in [1]: 1) intelligent signal processing, whichbuilds on real-time learning through continuous interaction ofthe radar with the surroundings; and 2) feedback from thereceiver to the transmitter, which is a facilitator of intelligence.

In summary, the CRR waveform design approach involvesthe following two steps:

• Step I: designing of the UWB-PPM waveforms in accor-dance with the communication data to be sent with anappropriate selection of the PPM delay (Section III);

• Step II: waveform selection based on the MI minimiza-tion approach to facilitate more effective target signatureextraction (Section IV).

III. STEP I: ULTRA-WIDEBAND PULSE-POSITION

MODULATION WAVEFORM DESIGN

In this section, we focus upon constructing the CRR wave-forms by introducing PPM to the column vectors of theWalsh–Hadamard matrix used in accordance with the data to betransmitted. This is similar to adopting a combination of PPMand pulse amplitude modulation schemes for communicationand radar functionalities, respectively. As will be seen in thesimulation results, the introduction of excess delays into theradar pulses does not affect significantly the orthogonality withrespect to the cross correlation between various CRR wave-forms. The communication source and destination identitiesare embedded in the preamble of the data frame as mentionedearlier.

Fig. 2(a) demonstrates the proposed UWB-PPM schemefor a 16-bit data frame, where the polarity and delay ofthe UWB pulses are determined by the column vector ofthe Walsh–Hadamard code and the PPM data, respectively.Fig. 2(b) shows the cross correlation between a pair of distinctCRR signals in the presence of AWGN and the autocorre-lation of an arbitrary CRR waveform. The former gives riseto small values throughout the entire range of delay samples,whereas the latter exhibits a sharp peak at the zero delay. Thisobservation verifies the orthogonality between various CRRsignals even after the PPM operation. In this way, different CRRnodes can simultaneously share the same spectral resourcesas long as they use different column vectors to design theirwaveforms. Fig. 2(c) indicates the spectrum of an arbitrarilyselected CRR signal received at a reference distance of 20 mfrom the transmitting node operating at 4 GHz. The trans-mission power complies with the regulations set by the FCCmask for UWB wireless devices transmitting in outdoors andindoors, which is at −41.3 dBm/MHz in the frequency range of3.6–10.1 GHz [14].

A. PPM Delay Selection in CRR Waveforms

The performance of UWB-PPM communications in terms ofBER and throughput has been well investigated in the literature.Some of the more recent works include [15]–[17], where thetime hopping scheme for UWB-PPM has been analyzed. UWBcommunications offer high data rates for communications andgood immunity against multipath fading over short ranges.

In this paper, we use a simple UWB-PPM scheme, where wedesign the PPM delay used for sending “1” or “0” such thatthe BER of the communication link is significantly reduced.For the classic PPM without any polarity change in pulses, thetheoretical analysis in [18] and [19] shows that the Euclidean


distance defined for the separation between two radar pulsesrepresenting “1” and “0” is given by

d(ζ)=

√√√√1−{[

1− 4π

(ζ

T

)2+

4π2

3

(ζ

T

)4]exp

[−π

(ζ

T

)2]}(4)

where ζ is the PPM delay to be designed, d(ζ) is the Euclideandistance between the PPM symbols, and T represents the pulsewidth. Subsequently, when the separation between the two sym-bols representing “1” and “0” is large, it can easily be shownthat the polarity change introduced to individual UWB mono-cycles in the current work has no effect on the value of d(ζ).

As shown in [18], the best signal design is the one thatmaximizes the squared Euclidean distance. In other words, fora given choice of pulse width T , we choose the PPM delay ζthat maximizes d2(ζ). At the same time, we also ensure that theorthogonality between radar signals is maintained after choos-ing a particular ζ. Hence, we seek a value for ζ, which keepsthe cross correlation between the designed waveforms below apredetermined level such that the orthogonality between themis satisfied.

The BER for such a UWB-PPM scheme is given as [18]

Pe(ζ) = Q

(√λd2(ζ)

2

)(5)

where Pe is the probability of error at a signal-to-noise ratio(SNR) of λ, and Q(·) stands for the Marcum Q function. Asthe orthogonality between CRR waveforms is not distorted,we can adopt an iterative design algorithm that maximizes thetarget scene information and at the same time is capable ofmaintaining active communication links between CRR nodeswith an acceptable BER performance. The selection of PPMdelay ζ is based upon minimization of Pe

ζ̂ = argminζ

Pe(ζ) = argminζ

Q

(√λd2(ζ)

2

)(6)

under the constraint of keeping the cross correlation betweenthe designed waveforms below a prespecified threshold. Oncethe communication data have been embedded into the orthogo-nal codes, we then proceed to selecting from these waveformsthe best possible signal to be transmitted in the next time instantbased on the minimization of MI.

IV. STEP II: MUTUAL-INFORMATION-BASED

WAVEFORM SELECTION

The basic idea behind the MI minimization approach is thatwe intend to identify the best possible radar waveform for thenext time instant based upon the current received backscattersignal. As the radar channel is dynamic due to the fluctuation inRCS of the target and other factors such as Doppler shift causedby the relative motion of the target and the surrounding clutter,there is a need for a dynamic waveform design and selectionapproach to constantly gain information from the environment.

A. Target Impulse Response and Parameter Estimation

The radar receiver has complete knowledge of the transmit-ted waveform at all instants of time. Hence, we can use thisinformation to extract parameters like target impulse response,target channel covariance matrix RH, and noise variance Rn.Let yt and yt−1 be the received signal vectors at two successivetime instants. Using (2), we have

E(yTt yt

)=xT

t RHxt +Rn = σ2t (7)

E(yTt−1yt−1) =xT

t−1RHxt−1 +Rn = σ2t−1 (8)

where σ2t and σ2

t−1 represent the variances of the receivedsignals at the respective time instants.

Solving simultaneously (7) and (8), we can estimate thevalues for RH and Rn. These values will be used to generatethe estimate of yt+1 for all values of xt+1 ∈ S using (2), whereS is the ensemble of the transmitted waveforms. We will choosext+1 ∈ S based on the proposed MI minimization approach.

The estimation of the target channel covariance matrix andthe noise variance will be performed at every instance ofreception of yt, and their values will be updated and used togenerate new estimates for yt+1.

B. MI Minimization Between Successive Target Echoes

The MI between two random vectors yi and yj , denoted asMI(yi,yj), is a measure of the information that yi conveysabout yj , or equivalently, the information that yj conveysabout yi. If the two random vectors are statistically dependent,then the MI between them is high. Hence, if yt−1 and yt

represent two received backscatter signals at successive timeintervals, and they are statistically dependent (i.e., high MI),then we cannot expect significant gain in information aboutthe radar scene. We therefore desire to obtain uncorrelated andindependent data samples from the radar scene to acquire moreinformation about the target from scan to scan. Subsequently,we select only those waveforms for transmission that wouldproduce less statistically dependent backscatter signals fromthe same radar scene. In other words, we intend to find thebest transmission signal xt by selecting from the ensemble S

a waveform that would minimize the MI between the currentreceived target echo and the estimated echo in the next timeinstant.

Let yt = {yt,1, yt,2, . . . , yt,K} ∼ N (µt, σ2t ) be the received

signal vector, which is normally distributed over K sampleswith mean µt and variance σ2

t . Then, we can express the MIbetween the successive received signal vectors in subsequenttime instants as

MI(yt−1,yt) =

H(yt−1|xt−1) +H(yt|xt)−H(yt−1,yt|xt−1,xt) (9)

where the first term H(yt−1|xt−1) represents the average in-formation or entropy. By classical definition of entropy, it isthe measure of uncertainty in the received signal at the timeinstant t− 1 given the knowledge of the transmitted signalxt−1. The knowledge of the transmitted waveform is assumedto be present at all time instants. The other two terms in (9) aredefined similarly.


Let y represent the sequence of the kth (k = 1, 2, . . . ,K)sample of N successive received signal vectors. Therefore,y = {yt,k, yt−1,k, . . . , yt−N+1,k} follows a multivariate normaldistribution with mean vector µ and covariance matrix Σ. Thejoint probability density function (pdf) of y is

fy(y)=1

(√

2π)N |Σ| 12exp

[− (y−µ)TΣ−1(y−µ)

2

]. (10)

Subsequently, the joint entropy can be expressed as

H(y|x) = 12ln[(2πe)N |Σ|

]nats. (11)

A detailed proof of (11) is shown in Appendix. Followingthis, we can derive the joint entropy H(yt,yt−1|xt,xt−1) bysubstituting N = 2 in (11) as

H(yt,yt−1|xt,xt−1) =H (yt,k, yt−1,k|xt,k, xt−1,k)

=12ln[(2πe)2|Σ|

]nats. (12)

Let the covariance matrix Σ be represented as [20]

Σ =

[σ2t ρσtσt−1

ρσtσt−1 σ2t−1

](13)

where ρ is the correlation coefficient. Thus

|Σ| =σ2t σ

2t−1 − (ρσtσt−1)

2

=σ2t σ

2t−1(1 − ρ2). (14)

Let the univariate pdf of the received signal vector yt berepresented as

Ψ(y) =1√

2πσ2t

exp

[− (y − µt)

2

2σ2t

]. (15)

By definition of entropy

H (yt|xt) = −∫

Ψ(y) ln [Ψ(y)] dy

= −∫

Ψ(y)

[− (y − µt)

2

2σ2t

− ln

(√2πσ2

t

)]dy

=E[(y − µt)

2]

2σ2t

+12ln(2πσ2

t

)=

12+

12ln(2πσ2

t

)=

12ln(2πeσ2

t

)nats. (16)

Similarly, we can write

H (yt−1|xt−1) =12ln(2πeσ2

t−1

)nats. (17)

Thus, using (9), (12), (14), (16), and (17), we obtain

MI(yt−1,yt)

= H(yt−1|xt−1) +H (yt|xt)−H (yt−1,yt|xt−1,xt)

=12ln(2πeσ2

t−1

)+

12ln(2πeσ2

t

)− 1

2ln[(2πe)2|Σ|

]=

12ln(2πeσ2

t−1

)+

12ln(2πeσ2

t

)

− 12ln[(2πe)2σ2

t σ2t−1(1 − ρ2)

]= −1

2ln(1 − ρ2). (18)

We can estimate the correlation coefficientρt,t−1 = E[yT

t yt−1]/√

σ2t σ

2t−1. As mentioned in Section IV-A,

we can estimate the values for yt+1 over all possible values ofxt+1 ∈ S using (2). Thus, we can also form an estimate of all

the values of the corresponding ρt+1,t = E[yTt+1yt]/

√σ2t+1σ

2t

and choose the value for xt+1 that minimizes (18).The MI minimization approach can be expressed as

MI∗ = minxt+1∈S

−12ln(1 − ρ2) (19)

subject to the power constraint E(xTt+1xt+1) ≤ P0, where P0

is the power available at the transmitter.Following Sections III and IV, we can summarize the CRR

waveform design algorithm as follows:

1) The current RH and Rn can be estimated through suc-cessive measurements with orthogonal UWB radar wave-forms x ∈ S, as discussed in Section IV-A.

2) An estimate for yt+1 is obtained using (2) and Step 1. Wethen choose the PPM delay ζ as discussed in Section III-Aand design the UWB-PPM ensemble S. We also selectxt+1 ∈ S to be transmitted based on the MI minimizationapproach and (19).

3) The CRR waveform carrying the communication sourceand destination identity information in the preamble ofthe data frame is transmitted. The communication linkcan either be between two different CRR nodes or be-tween a CRR node and a remote sensor monitoring thephenomenon of interest induced by the target.

4) The received radio signals are collected and passedthrough a matched filter bank, which uniquely identifiesthe orthogonal sequence out of the ensemble S and de-modulates the PPM signal.

5) The received radar signals are used to extract the targetparameters like target range, velocity, and impulse re-sponse. The estimates for RH and Rn are updated usingthe current received signal and are relayed back to the MIminimization module.

6) The process is repeated iteratively.

The cognitive operation involved in the proposed strategy canthus be summarized as follows:

1) The system constantly updates its estimate on the targetimpulse response by continual measurements of the radarenvironment and utilizes this information to select thebest possible waveform for transmission. In this way, thewaveform selection approach learns continuously frommultiple interactions with the radar scene and utilizesthe extracted target information to make the selection ofthe subsequent transmission waveforms. A feedback loopfrom the receiver to the transmitter allows the delivery ofthis radar scene information to the transmitter.


2) The system adapts its UWB-PPM interpulse duration andthus adjusts its operational mode in accordance with thetarget parameter variation “on the fly.”

Such architectures are similar to cognitive radars, where thesystems adopt a constant learning approach, as described in [1].

V. SIMULATION RESULTS AND DISCUSSION

A. Simulation Setup

For simulation purposes, we set the PPM delay ζ suchthat the cross-correlation coefficient between the transmissionwaveforms is no greater than 0.4. In this way, we have anacceptable BER for communications. Furthermore, the orthog-onality between distinct CRR waveforms is maintained forradar waveform optimization purposes. The received signal ismatched filtered to estimate the propagation delay. The PPMdata are demodulated separately, and the radar signal processingis carried out by the target impulse response and parameterestimation module. As described in the previous sections, thetarget channel covariance matrix is estimated and the noise vari-ance is also determined to help the MI minimization module todecide upon the best UWB sequence to be used for transmissionin the subsequent time interval. The center frequency of thetransmission UWB pulses is 4 GHz and the sampling frequencyis 10 GHz.

B. Target Range Estimation

To estimate the target range or equivalently the time of arrival(TOA) of the backscatter signal, it is essential to determine thevalue of the pulse repetition interval (PRI) between successiveGaussian monocycles in a UWB sequence. We apply the mul-tipath channel model as mentioned in [10] with the presence ofan LOS path as the major component in the backscatter signal.

To find the target distance, the CRR node will estimate theactual time delay τd using correlation. The transmitted signal isdelayed by time τn and is cross correlated with the target returnas follows:

Ryx(τd − τn) = E[xTt (τ − τn)yt(τ − τd)

]. (20)

To estimate the time delay, the maximum value of Ryx is eval-uated by varying the value of τn at the receiver. Fig. 3(a) showsthe range profiles of the target and the surrounding clutter. Forthis simulation, the target and the clutter are assumed to bestationary. This profile is achieved by continually probing thestationary radar environment with CRR waveforms chosen bythe MI minimization algorithm. At each iteration, we select thewaveform that would produce the least correlated output. Therange resolution result in Fig. 3(a) is obtained at the end of 10such iterations of MI minimization. As seen from this figure,the target is located at a distance of approximately 38 m fromthe CRR node. Subsequently, three or more distinct CRR nodescan share the information of the target relative distance andtriangulate its location over the communication links. Fig. 3(b)indicates the range profile of the radar scene for a duration of4 s, where the target remains distinguishable from those nontar-get scatterers throughout the entire period.

Fig. 3. (a) Static target and nontarget (clutter) scatterers resolved after10 iterations of MI minimization at a CRR node. (b) Target and clutter returnsafter 10 iterations of MI minimization.

C. MI Minimization and Target Detection Probability

Fig. 4(a) demonstrates the MI minimization process fordifferent SNRs. At high SNRs, the channel covariance matrixRH can successfully be estimated, and therefore, the value ofMI decreases as the number of iterations increases. On the otherhand, the estimation of RH is poor at low SNRs. Consequently,the best transmission sequence for the next time instant isnot always selected, and the MI does not exhibit significantdecrease even with more iterations of the algorithm. Hence,there would be little gain in the information pertaining to thetarget scene, and the waveform selection approach would fail toprovide performance improvement in this case.

Fig. 4(b) indicates the probability of target detection in thepresence of AWGN and clutter interference. We use the per-formance measure of signal-to-clutter-and-noise ratio (SCNR)to evaluate the detection probability. This measure can beexpressed as [21, Chapter 6]:

SCNR =1

1SNR + 1

SCR

(21)

where SCR is the signal-to-clutter ratio and can be evaluatedfollowing the result in [22]. For a particular CRR waveformand a stationary radar scene, 1000 simulations have been run foreach SCNR, and the probability of successful target detectionis plotted based on the hypothesis testing method employingthe optimal Neyman–Pearson detector algorithm in [12] for a


Fig. 4. (a) Minimization of MI algorithm at different SNRs. (b) Probability of target detection against SCNR for various iterations of the MI minimizationalgorithm. (c) Probability of detection for waveform selection based on MI minimization and static waveform assignment.

fixed false positive rate of 10−5. Then, the next CRR waveformis chosen according to the MI minimization algorithm, and theprocess is repeated for 50 iterations. As seen from Fig. 4(b), theMI minimization algorithm converges after 50 iterations, yield-ing a detection probability of 0.9 at SCNR = 8 dB as comparedto SCNR = 17 dB at the first iteration. However, as we increasethe number of iterations, the probability of detection does notshow further improvement after 50 iterations.

In Fig. 4(c), we compare the probability of target detectionfor varying waveforms selected by the MI minimization algo-rithm to the probability for an arbitrary static waveform used toestimate the target parameters over multiple snapshots. As theproposed approach always chooses distinct waveforms, whichwould produce received signals having low correlation overtime, the system adapts its probing signal better to the fluctu-ating target RCS. The static waveform on the other hand, inspite of multiple iterations, is unable to match the time-varyingtarget response. Hence, the probability of target detection issuboptimal in this case.

D. Communication BER and Throughput Performance

Fig. 5(a) shows the performance of the CRR waveform de-sign for a single CRR node from a communications perspective.

The plot indicates the BER for different systems based onsimulation results obtained with both Matlab and Simulink op-erating platforms, which have been investigated in the literaturefor joint communication–radar networks. The proposed CRRwaveform solution can be modified by incorporating additionalmodulation levels for PPM, e.g., 16- or 4-ary PPM. However,as we go on increasing the delay between the radar pulsesto send a larger constellation of signals, the orthogonalityof the UWB sequences is affected. Specifically, we observethat as the interpulse delay is increased, the sidelobes in theautocorrelation plot become more prominent. This distorts theorthogonality of the UWB-PPM waveform and in turn maydeteriorate the performance of the radar receiver subsystem.Consequently, choosing the appropriate Euclidean distance orthe delay for PPM results in a tradeoff between communicationand radar signal design requirements. As seen from the plot andalso described in [7]–[9], UWB-OFDM signals offer better biterror performance when we adopt data redundancy bits for errorcontrol. However, the proposed UWB-PPM design performscomparably to UWB-OFDM schemes when no redundant bitis added.

Fig. 5(b) shows the throughput analysis for the CRR wave-form as compared to other UWB-OFDM signal designs. UWBcommunications in general achieve high data rate over short


Fig. 5. (a) BER of different joint communication–radar waveform designs.(b) Throughput performance against distance from a particular CRR node.

distances, as the distance between the communicating nodesincreases the throughput falls. The proposed design offers a datarate of just about 200 Mb/s at a distance of 20 m, which is betterthan that offered by the four-carrier uncoded UWB-OFDM.

E. Mobile Target Scene Simulation

Fig. 6(a) shows the target and clutter range profiles for adynamic radar environment, in which the target and cluttersources are in relative motion with respect to the observingCRR node. The simulation has been carried out with a relativevelocity of 3.5 m/s. By choosing CRR waveforms based onthe estimated target impulse response and ensuring that ateach instance of reception the received signals are uncorrelatedfrom each other, the proposed MI minimization algorithm isable to achieve resolvable target and clutter returns at the10th iteration, as shown in Fig. 6(a). In Fig. 6(b), we observethat the target and the surrounding clutter are distinctly resolvedinto different range bins even if the radar scene is dynamic. TheMI minimization algorithm is self-corrective since it updatesthe estimated value of the target channel covariance matrixand also the noise variance at each step. This result demon-strates the effectiveness of the proposed MI minimization al-gorithm for dynamic radar scenes with mobile targets andclutter.

F. Impact of Multipath Channel on System Performance

In this section, we study the effect of multipath propagationon the performance of the proposed system. Fig. 7(a) displays

Fig. 6. (a) Target range profile for a target velocity = 3.5 m/s for 4-s timeduration after 10 iterations of MI minimization. (b) Target and clutter returnsafter 10 iterations of MI minimization.

the average power delay profile of the UWB channel modelmentioned in [10], which is used throughout the simulations.The excess delay is measured relative to the first arrival, andthe vertical axis denotes the energy level of each delay bin.On average, over 92% of the total energy is confined within100 ns. This means that a PRI greater than 100 ns would ex-perience very little intersymbol interference (ISI). In addition,over 99% of the total energy arrives within 160 ns. We set thevalue of PRI to be above 200 ns for our system to avoid ISI.

As described in Section V-B, we intend to estimate the delayτ that maximizes Ryx. We therefore perform the peak detectionon Ryx to obtain an estimate for the TOA and hence thedistance of the target. In Fig. 7(b), we compare the ranging per-formance based on the mean TOA from the received signal andthe TOA determined via the peak detection of Ryx. Apparently,this result indicates the ranging error with respect to the TOAvariation caused by the multipath dispersion. As seen from thefigure, at low SCNRs, the error using TOA obtained from thepeak detection of Ryx is smaller than that using the mean TOA.We also compare this result with the Cramer–Rao lower bound(CRLB) for the TOA estimation method in [23]. It can be foundthat the proposed scheme achieves errors close to CRLB at highSCNRs.

Fig. 7(c) shows the effect of PRI on the ranging performance.As seen from this figure, the ranging error sharply increasesas we decrease the PRI below 200 ns. This complies with


Fig. 7. (a) UWB channel model as shown in [10]. (b) Average ranging error based on TOA estimation in the multipath UWB channel. (c) Average ranging erroragainst PRI in the multipath UWB channel.

the previous analysis on the delay interval within which theincoming signal power is confined. Finally, the proposed tech-nique outperforms the method based on the mean TOA through-out the entire range of PRI.

VI. CONCLUSION

We have developed a joint communication–radar waveformdesign solution for the CRR network, where the radar andcommunication signals share the same spectral and temporaldomains. As indicated by the simulation results, the CRR wave-form optimization approach promises better target impulse re-sponse extraction and range resolution. From a communicationperspective, the proposed strategy also promises high data rateperformance over short ranges. This approach is based uponconstant learning about the target environment and adaptingthe transmission waveform characteristics to suit the dynamictarget scene. Such a cognitive strategy ensures maximum in-formation extraction from the radar scene and better targetdiscrimination capability. The proposed unified system wouldconstitute a unique cost-efficient platform for future intelligentsurveillance applications for which both environmental sensingand establishment of ad hoc communication links are essential.Such systems can be used in mission-critical applications tosimultaneously address the remote surveillance and communi-cation issues. It is envisioned that future personal communica-tion and tracking devices will have comprehensive radar-likefunction, such as spectrum sensing and localization, in additionto multimode and multiband communication capabilities.

APPENDIX

PROOF OF (11) IN SECTION IV-B

Let us assume that y, as defined in Section IV-B, followsa multivariate normal distribution with mean vector µ andcovariance matrix Σ. Thus, the joint pdf of y is

fy(y)=1

(√

2π)N |Σ| 12exp

[− (y−µ)TΣ−1(y−µ)

2

]. (22)

Subsequently, we have

H(y|x) = −∫

fy(y)

{− (y − µ)TΣ−1(y − µ)

2

− ln

[(√2π

)N

|Σ| 12]}

dy

=12E

∑

i,j

(yi − µi)(Σ−1)ij(yj − µj)

+12ln[(2π)N |Σ|

]=

12E

∑

i,j

(yi − µi)(yj − µj)(Σ−1)ij

+12ln[(2π)N |Σ|

]=

12

∑i,j

E [(yj − µj)(yi − µi)] (Σ−1)ij

+12ln[(2π)N |Σ|

]


=12

∑j

∑i

Σji(Σ−1)ij +

12ln[(2π)N |Σ|

]

=12

∑j

(ΣΣ−1)jj +12ln[(2π)N |Σ|

]

=12

∑j

Ijj +12ln[(2π)N |Σ|

]

=N

2+

12ln[(2π)N |Σ|

]=

12ln[(2πe)N |Σ|

]nats. (23)

This completes the proof.

REFERENCES

[1] S. Haykin, “Cognitive radar: A way of the future,” IEEE Signal Process.Mag., vol. 23, no. 1, pp. 30–40, Jan. 2006.

[2] Y. Chen and P. Rapajic, “Ultra-wideband cognitive interrogator network:Adaptive illumination with active sensors for target localisation,” IETCommun., vol. 4, no. 5, pp. 573–584, Mar. 2010.

[3] Y. Nijsure, Y. Chen, P. Rapajic, C. Yuen, Y. H. Chew, and T. F. Qin,“Information-theoretic algorithm for waveform optimization within ultrawideband cognitive radar network,” in Proc. IEEE ICUWB, Sep. 2010,vol. 2, pp. 1–4.

[4] M. Bell, “Information theory and radar waveform design,” IEEE Trans.Inf. Theory, vol. 39, no. 5, pp. 1578–1597, Sep. 1993.

[5] Y. Yang and R. S. Blum, “Radar waveform design using minimum mean-square error and mutual information,” in Proc. 4th IEEE SAM, Waltham,MA, 2006, pp. 234–238.

[6] Y. Yang, R. S. Blum, Z. S. He, and D. R. Fuhrmann, “MIMO radarwaveform design via alternating projection,” IEEE Trans. Signal Process.,vol. 58, no. 3, pp. 1440–1445, Mar. 2010.

[7] S. C. Surender and R. M. Narayanan, “UWB noise-OFDM netted radar:Physical layer design and analysis,” IEEE Trans. Aerosp. Electron. Syst.,vol. 47, no. 2, pp. 1380–1400, Apr. 2011.

[8] D. Garmatyuk, J. Schuerger, Y. Morton, K. Binns, M. Durbin, andJ. Kimani, “Feasibility study of a multi-carrier dual-use imaging radarand communication system,” in Proc. EuRAD, 2007, pp. 1473–1476.

[9] C. Sturm, T. Zwick, and W. Wiesbeck, “An OFDM system concept forjoint radar and communications operations,” in Proc. IEEE 69th VTC,Spring 2009, pp. 1–5.

[10] D. Cassioli, M. Z. Win, and A. F. Molisch, “The ultra-wide bandwidthindoor channel: From statistical model to simulations,” IEEE J. Sel. AreasCommun., vol. 20, no. 6, pp. 1247–1257, Aug. 2002.

[11] A. Rihaczek, Principles of High-Resolution Radar. Marina del Rey, CA:Mark Resources, 1977.

[12] M. Skolnik, Introduction to Radar Systems, 2nd ed. New York:McGraw-Hill, 1980.

[13] N. A. Goodman, P. R. Venkata, and M. A. Neifeld, “Adaptive waveformdesign and sequential hypothesis testing for target recognition with activesensors,” IEEE J. Sel. Topics Signal Process., vol. 1, no. 1, pp. 105–113,Jun. 2007.

[14] “Federal Communications Commission: Revision of Part 15 of theCommission’s Rule Regarding Ultra-Wideband Transmission Systems,First Report and Order,” FCC, Washington, DC, Tech. Rep. ET Docket98_153, FCC02-48, 2002.

[15] W. Wu, J. Rong, J.-S. Hong, and B.-Z. Wang, “Performance of UWBPPM-TH system with time reversal and its improved solution one-bit TR,”in Proc. ISPACS, Dec. 2010, pp. 1–4.

[16] Y.-S. Shen, F.-B. Ueng, J.-D. Chen, and S.-T. Huang, “A performanceanalysis of the high-capacity TH multiple access UWB system usingPPM,” in Proc. IEEE 20th PIMRC, Sep. 2009, pp. 2454–2458.

[17] N. V. Kokkalis, P. T. Mathiopoulos, G. K. Karagiannidis, andC. S. Koukourlis, “Performance analysis of M-ary PPM TH-UWB sys-tems in the presence of MUI and timing jitter,” IEEE J. Sel. AreasCommun., vol. 24, no. 4, pp. 822–828, Apr. 2006.

[18] F. Ramirez-Mireles, “Signal design for ultra wideband PPM communica-tions,” in Proc. IEEE MILCOM, Oct. 2002, vol. 2, pp. 1085–1088.

[19] F. Ramirez-Mireles, “On the capacity of UWB over multipath channels,”IEEE Commun. Lett., vol. 9, no. 6, pp. 523–525, Jun. 2005.

[20] T. M. Cover and J. A. Thomas, Elements of Information Theory. NewYork: Wiley-Interscience, 2006.

[21] B. R. Mahafza and A. Z. Elsherbeni, Matlab Simulations for RadarSystems Design, 1st ed. Boca Raton, FL: CRC Press, 2005.

[22] R. McMillan and I. Kohlberg, “A probabilistic model of the radar signal-to-clutter and noise ratio for Weibull-distributed clutter,” in Proc. IEEERadar Conf., May 2010, pp. 882–886.

[23] J.-Y. Lee and S. Yoo, “Large error performance of UWB ranging in mul-tipath and multiuser environments,” IEEE Trans. Microw. Theory Tech.,vol. 54, no. 4, pp. 1887–1895, Jun. 2006.

Yogesh Nijsure received the B.E. degree (with Dis-tinction) in electronics engineering from the Univer-sity of Mumbai, Mumbai, India, in 2006 and theM.Sc. degree (with Distinction) in wireless commu-nication systems engineering from the University ofGreenwich, London, U.K., in 2008. He is currentlyworking toward the Ph.D. degree with NewcastleUniversity, Newcastle upon Tyne, U.K.

From March 2010 to September 2010, he un-dertook his research internship as a Research En-gineer with the Institute for Infocomm Research,

Singapore. Since November 2011, he has been a Research Fellow with theNanyang Technological University, Singapore. His research interests includecognitive radar network design, Bayesian nonparametric methods for wirelesssensor network applications, ultra-wideband radar systems, cognitive radionetworks, wireless communication theory, information theory, and radar signalprocessing.

Yifan Chen (M’06) received the B.Eng. (Hons I) andPh.D. degrees in electrical and electronic engineer-ing from Nanyang Technological University (NTU),Singapore, in 2002 and 2006, respectively.

From 2005 to 2007, he was a Research Fellowwith the Singapore-University of Washington Al-liance in Bioengineering, NTU, and the Universityof Washington at Seattle. From 2007 to 2010, hewas a Senior Lecturer with the School of Engineer-ing, University of Greenwich, London, U.K. He iscurrently a Lecturer with the School of Electrical

and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.His current research interests involve wireless and pervasive communicationsfor healthcare, nano-inspired applications of electromagnetics in medicineand biotechnologies, microwave biomedical imaging, wireless propagationand network channel modeling, cognitive wireless systems and networks, andwireless communication theory.

Dr. Chen received the Early Career Research Excellence Award in 2009 andthe Promising Research Fellowship in 2010 from the University of Greenwichin recognition of his exceptional research contributions and potential.

Said Boussakta (S’89–M’90–SM’04) received the“Ingenieur d’Etat” degree in electronic engineer-ing from the National Polytechnic Institute of Al-giers (ENPA), Algiers, Algeria, in 1985 and thePh.D. degree in electrical engineering (signal andimage processing) from the Newcastle University,Newcastle upon Tyne, U.K., in 1990.

From 1990 to 1996, he was a Senior ResearchAssociate in digital signal and image processing withthe Newcastle University. From 1996 to 2000, he wasas Senior Lecturer in communication engineering

with the University of Teesside, Teesside, U.K. From 2000 to 2006, he wasa Reader in digital communications and signal processing with the Universityof Leeds, Leeds, U.K. He is currently a Professor of communications andsignal processing with the School of Electrical and Electronic Engineering,Newcastle University, where he lectures on advanced communication networksand signal processing subjects. He has authored and coauthored more than 250publications. His research interests are in the areas of fast DSP algorithms,digital communications, communications networks systems, cryptography andsecurity, and digital signal/image processing.

Dr. Boussakta is a Fellow of the Institution of Engineering and Technology.


Chau Yuen (M’08) received the B.Eng. and Ph.D.degrees from Nanyang Technological University,Singapore, in 2000 and 2004, respectively.

He was a Postdoctoral Fellow with Lucent Tech-nologies Bell Labs, Murray Hill, NJ, in 2005 anda Visiting Assistant Professor with the Hong KongPolytechnic University, Kowloon, Hong Kong, in2008. From 2006 to 2010, he was a Senior ResearchEngineer with the Institute for Infocomm Research,Singapore. Since June 2010, he has been an AssistantProfessor with the Singapore University of Technol-

ogy and Design. He has published over 80 research papers in internationaljournals or at conferences. His present research interests include green commu-nications, cognitive network, cooperative transmissions, and network coding.

Dr. Yuen serves as an Associate Editor for IEEE TRANSACTIONS ON

VEHICULAR TECHNOLOGY.

Yong Huat Chew (M’97) received the B.Eng.,M.Eng., and Ph.D. degrees in electrical engineeringfrom the National University of Singapore (NUS),Singapore.

Since 1996, he has been with the Institutefor Infocomm Research (formerly known as Centrefor Wireless Communications, NUS, and Institutefor Communications Research), Singapore, an in-stitute under the Agency for Science, Technologyand Research, where he is currently a Senior Sci-entist. He has also held an Adjunct Position with

the Department of Electrical and Computer Engineering, NUS, since 1999,and is currently an Adjunct Associate Professor. His research interests arein technologies related to high spectrally efficient wireless communicationsystems.

Zhiguo Ding (S’03–M’05) received the B.Eng. de-gree in electrical engineering from the Beijing Uni-versity of Posts and Telecommunications, Beijing,China, in 2000 and the Ph.D. degree in electricalengineering from Imperial College London, London,U.K., in 2005.

From July 2005 to June 2010, he was with Queen’sUniversity Belfast, Belfast, U.K., Imperial College,and Lancaster University, Lancaster, U.K. Since June2010, he has been a Lecturer with Newcastle Uni-versity, Newcastle upon Tyne, U.K. His research in-

terests include cross-layer optimization, cooperative diversity, statistical signalprocessing, and information theory.

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