Extended time processing for passive bistatic radar

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1&

Published in IET Radar, Sonar and NavigationReceived on 5th November 2012Revised on 24th July 2013Accepted on 31st July 2013doi: 10.1049/iet-rsn.2012.0321

012The Institution of Engineering and Technology 2013

ISSN 1751-8784

Extended time processing for passive bistatic radarGraeme E. Smith1, Kevin Chetty2, Christopher John Baker1, Karl Woodbridge3

1Department of Electrical & Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus,

Ohio 43210, USA2Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WCH 9EZ, UK3Department of Electronic & Electrical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK

E-mail: [email protected]

Abstract: The authors present a novel optimisation of calculation of the range-velocity surface for passive bistatic radar (PBR).Unlike other optimisations, the time-bandwidth product is maintained by ensuring that maximum integration gain is achieved.The advocated technique also permits extended observation intervals without increased processing. Continuous signals ofopportunity are interrupted in the receiver; this enables high coherence over extended data acquisition times facilitating highDoppler/velocity resolution. The effect of this technique on integration gain and robustness to target decorrelation isinvestigated using a simulation. A validating experiment is reported in which a prototype PBR obtains a velocity resolution of0.07 ms−1 when measuring a human target.

1 Introduction

Ever increasing pressure on the usage of the electromagnetic(EM) spectrum passive bistatic radar (PBR) [1, 2] isbecoming a progressively more important surveillancetechnology suitable for an array of applications [3–7]. In thePBR, an illuminator of opportunity replaces the dedicatedtransmitter of the conventional radar. The PBR uses thissignal to achieve target detection, localisation and tracking.The illuminator of opportunity can be any other user of theEM spectrum and its transmission does not have to beintended for radar use. Recently, there has been muchinterest in using wideband digital network signals, such asthe 802.11 wireless network signal [8], as the basis for aPBR. The wider bandwidths of these signals naturallyprovide improved bistatic range resolution and the dataprocessing requirements are also increased to potentiallyintractable levels. In this paper, we propose an alternativePBR data processing method that helps mitigate theexpanded data processing requirements without reducingsystem performance.Early PBR research concentrated on air surveillance roles

and there have been several successful demonstrations of airtarget detection and tracking together with systemperformance analysis [4, 6, 9–15]. A typical configurationuses a commercial broadcast transmitter and two receivechannels. One receive channel would sample the signalreceived directly from the transmitter of opportunity, whichacts as a reference waveform. This is known as the‘reference’ channel. The other, ‘surveillance’ channel isused to detect echoes from targets. FM radio, because of itsuniversal availability and high transmit powers, is oftenused as the illuminator of opportunity. However, otherbroadcasts, such as TV or digital radio, have also been

used. FM-based PBR is a maturing technology, a sign ofwhich is successful trials conducted using an airbornereceiver system [4, 11, 16]. PBR bandwidths are relativelynarrow (e.g. the FM bandwidth is at most 150 kHz) leadingto poor range resolution. Hence, most systems performtarget detection using Doppler domain processing. In otherwords, targets are resolved from each other and from clutterbased upon their relative velocity rather than using spatialor range resolution.More recently, research has been reported that utilises

802.11 wireless network transmissions as the illuminator ofopportunity for surveillance in and around buildings [3, 17–20]. There have been successful trials showing detection ofpeople within buildings [18], through-the-wall detection [3]and cross-range resolution enhancement through applicationof imaging techniques [19]. Much of this research focusedon suppression of direct signal interference (DSI). DSI isthe signal that is received in the surveillance channel thatcomes directly from the illuminator of opportunity. Itcompetes with the faint echoes from targets and can be asmuch as 100 dB above the receiver noise floor thus severelyreducing system sensitivity. Consequently, to remove theDSI, sophisticated adaptive filters have been developed [21]as well as simpler, iterative subtraction methods based onthe CLEAN algorithm [3].PBRs using an 802.11 wireless network have an increased

signal bandwidth and this has a significant impact on dataprocessing times. The effective bandwidth of an 802.11 gwireless network signal is around 16 MHz [8], far greaterthan 150 kHz of FM radio. However, these largerbandwidths combined with long observation intervals canresult in substantial data processing requirements. Forexample, if a two channel PBR is considered and the ADCsstore their samples as 16-bit unsigned integers then for a 1 s

IET Radar Sonar Navig., 2013, Vol. 7, Iss. 9, pp. 1012–1018doi: 10.1049/iet-rsn.2012.0321

Fig. 1 Schematic of a passive bistatic radar

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observation the system will have to transport over data links/buses, store and process a minimum of 120 MB of data.This paper describes a method for achieving the extended
observation intervals of a PBR while controlling the amountof data that has to be processed. The new data processingtechnique trades integration gain for reduced data overhead.Despite this trade, strong target responses are obtainedcompared with conventional radar systems, since, incommon with other PBR processing methods, the durationof the illuminating signal is much greater than the shortpulse of a conventional pulse-Doppler radar. For example,PBRs integrate on timescales of the order of secondswhereas pulse-Doppler radars integrate for mill-seconds(and then not continuously). The viability of the techniqueis also demonstrated in the presence of target decorrelation.The remainder of this paper is arranged as follows. In

Section 2, the extended time processing technique ispresented. In Section 3, a prototype indoor PBR, operatingwith 802.11 g wireless network system, is employed toexamine the performance of extended time processing usingreal data and this is compared with performance derivedfrom more conventional approaches. Results illustrating theimpact on the ambiguity surface, on integration gain underthe effects of different target decorrelations are presentedand discussed using both experiments and simulations. Asummary is given and conclusions drawn in Section 4.

2 Extended time processing for PBR

In this section, we introduce the concept of extended timeprocessing for the PBR. The basic concepts behind PBR aresummarised and efficient methods for calculating therange-velocity surface discussed. We then show how thesemethods can be modified to allow extended time processingwithout necessarily increasing the computational overhead.

2.1 Radar geometry

A fundamental component of the PBR is the calculation of arange-velocity (or range-Doppler) surface using thecross-ambiguity function, [1] and [2], between the directand surveillance channels. The range-velocity surface isfurther processed via thresholding to detect targets enablingthe subsequent formation of tracks. A schematic of the PBRconcept is shown in Fig. 1.The cross-ambiguity between the two channels is

calculated as [1, 2, 12, 22]

x t, fD( ) =

∫1−1

s(t)r∗(t + t)e−j2pfDtdt (1)

where s(t) is the surveillance signal, r*(t + t) is the conjugateof the reference signal shifted by t, fD is the frequency shiftand t is the time. If the PBR to target geometry is known,the frequency variable fD can be converted into a velocityaxis, giving the range-velocity surface, through thefollowing expression

v = fDlc{ }

/{2 cos (b/2) cos (d)} (2)

where λc is the carrier wavelength, β is the bistatic angle and δthe angle between the target direction of travel and the bistaticbisector.If the sole reflecting object in the scene of interest is a

point scatterer, then r(t) = u(t − tRef) and s(t) =


au(t − tTgt)e j2pfTgtt. Here, u(t) is the signal transmittedby the illuminator of opportunity, tRef the delay resultingfrom the line of sight (LOS) distance between theilluminator and the reference receiver, tTgt the delayresulting from the bistatic distance between the illuminator,target and surveillance receiver, fTgt the bistatic Dopplershift induced by the target motion and a the complexamplitude incorporating propagation losses and targetreflectivity. If the speed of propagation, location of theilluminator and the receivers and angle of the referencereceiver antenna are known, it is straightforward to convertthe values of fD and t corresponding to the target peak in(1) into Cartesian co-ordinates [1].

2.2 Calculation of the range-Doppler surface

A principal challenge facing all PBR systems is the efficientcalculation of (1). The duration of the signals s(t) and r(t)can be several seconds. Even for modest bandwidthsystems, this results in a large number of digital samplesand direct calculation of (1) is not possible in real timeunless very expensive bespoke processing systems areadopted. Methods for optimising the calculation of (1) havebeen presented in [6, 12, 23–25] that operate throughoptimisation in the frequency domain. For each range delay,t, of interest the term s(t)r*(t + t) from (1) is treated as asignal being discrete Fourier transformed (DFTed) and χ(t,fD) is calculated one delay at a time. Typically, the samplerate of the signals is much greater than the Doppler shifts ofinterest hence s(t)r*(t + t) can be downsampled beforeperforming the DFT which significantly reduces theprocessing requirements. However, this downsampling hasa secondary effect of reducing the time-bandwidth productand hence, reducing the integration gain [26]. Furthermore,increasing the observation time to improve velocityresolution will increase the number of samples processedwith this technique and hence, increase the processingrequirements.Here, we introduce an alternative method for calculating

the cross-ambiguity surface that optimises in the timedomain. Time domain optimisation facilitates new modes ofPBR operation. Specifically, it enables improved Doppler/velocity performance without increasing the number ofsignal samples collected. In essence, the digitisation ofcontinuous transmission is interrupted in the receiver tomimic pulsed operation. This has the advantage of keeping

1013& The Institution of Engineering and Technology 2013

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coherence high while supplying longer data acquisition timeswith a reduced number of samples.A digital pulse-Doppler radar produces a range-Doppler
surface using a pulse train collected over a coherentprocessing interval (CPI) [2, 27]. For each pulse, thebackscatter signal is digitised for the delays correspondingto range and extent of the scene of interest. The digitisedreceived signal is pulse compressed to obtain a rangeprofile. The range-Doppler surface is then obtained bytaking the DFT of the complex values in each range bin ofthe profile. The PBR also measures the backscatter signalover the CPI. However, its measurement of the backscattersignal is continuous rather than being constrained to thedelays corresponding to the scene of interest and thisgreatly increases the processing overhead. While techniquessuch as those presented in [6, 12, 23–25] optimise thecalculation of (1) they still start with digitisation for theduration of the CPI before reducing the number of samplesin the frequency domain. The technique advocated here isinspired by the pulse-Doppler radar and reduces the numberof samples recorded during the CPI to achieve itsoptimisation of the calculation of (1). Such an optimisationallows fine Doppler resolution associated with PBR whileoperating on a reduced number of samples thus improvingprocessing speed and reducing storage requirements.Assume that the PBR sampling frequency is fSamp and

TSamp = 1/fSamp then the digitised surveillance signal can bestored in a vector s and the reference signal in a vector rdefined as

s W s1 s2 . . . sN[ ]T

, sn = s (n− 1)TSamp

( ), s [ CN

r W r1 r2 . . . rN[ ]T

, rn = s (r − 1)TSamp

( ), r [ CN

(3)

where N is the total number of samples collected in the CPIand superscript T indicates the transpose operation. Thecontent of the vectors can be considered a concatenation ofseveral lower dimension vectors

s = sT1 sT2 · · · sTM[ ]T

r = rT1 rT2 · · · rTM[ ]T (4)

whereM =N/P, P is the number of elements in the contiguouslower dimension vectors, that is sm, rm [ C

P, and is anoperator that rounds to the nearest integer. The value of Pshould be set based on the properties of the desiredrange-Doppler surface. The duration of each lowerdimension vector is PTSamp, hence the unambiguous regionof frequency covered by χ(t, fD) would have an extent of 1/PTSamp. The maximum possible extent of the bistatic rangecovered will be cPTSamp, where c is the speed of propagation.To obtain the range-Doppler surface, χ(t, fD), the

individual lower dimension vectors must be cross-correlatedand then a DFT performed at each bin of the output. Forany pair of lower dimension vectors sm and rm a bistaticrange profile, um, can be calculated according to

uk =∑2Pp=1

s̃pr̃∗p+k where k = 0 . . .K and K ≤ P (5)

and where uk are the elements of um and s̃p and r̃p are


elements from versions of sm and rm that have beenzero-padded to length 2P. The bistatic range covered by umwill be cKTSamp. By defining a matrix of bistatic rangeprofiles, U = [ u2 u2 . . . uM ]T, the range-Dopplersurface can be calculated as

x t, fD( ) = DFT{U} (6)

where DFT{·} is a discrete Fourier transform operator thatacts along the columns of a matrix and the surface χ(t, fD)is quantised such that t is sampled every TSamp seconds andfD is sampled every 1/KTSamp Hz. To control the Dopplersidelobes, a windowing function can be applied to thecolumns of U. We used a simple Hanning window,however, this selection is not critical to the technique. It isimportant to note that the number of digital samples utilisedin calculating (6) is the same as (1) assuming that theremainder of N/P is zero. Since the cross-correlation andDFT operations are linear, the order in which they areperformed does not affect the coherent integration gainachieved, which is equal to the time-bandwidth product ofthe signal [26]. Essentially, the duration of the signal beingprocessed and the time part of the time-bandwidth, is thesame between the two techniques. Calculation of (6) isfaster than direct calculation of (1), since the lengths of theDFTs required are greatly reduced.Extended time processing is implemented by interrupting

the recording of digital samples in the PBR such that thelower dimension vectors sm and rm no longer formcontiguous sets. The original signal vectors s and r havebeen replaced by sets of vectors {sm} and {rm}, m = 1 …M, that are analogous to a train of pulses from apulse-Doppler radar. These sets of vectors can still be usedto perform the calculations of (5) and (6), except that thefrequency resolution of the resulting range-Doppler surface is

DfD = 1/ MTRep

( )(7)

where TRep is the repetition interval of the vectors in the sets{sm} and {rm}.This simple modification to the calculation of the

range-Doppler surface allows fine Doppler resolution (andhence velocity resolution) to be achieved without increasingthe processing overheads. Assuming that the target does notdecorrelate during the extended CPI, the time-bandwidthproduct, and hence, integration gain, of the extended timeprocessing are proportional to the number of samplesintegrated, which is MP. Since all the MP samples areemployed in calculating the extended time range-Dopplersurface, the gain will be the same as if a continuous signalhad been used, but with a duration of MP/fsamp. The powerof this approach becomes apparent if the number of samplesrecorded by extended processing is reduced. If the durationof the individual sm and rm vectors is reduced, that is P ismade smaller, then less samples are recorded during theCPI, however, the duration of the CPI remains the same.The Doppler resolution will be maintained, since it relies onthe CPI duration. The integration gain will be reduced,since MP, is smaller, however, in the PBR, integrationgains are typically very high hence, the loss may beconsidered tolerable.


Fig. 3 Ambiguity surfaces for

a Regular processing andb Extended time processing methods

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3 Results and analysis
In this section, we analyse the performance when usingextended time processing compared with the time domainoptimised calculation of (1) described in (3) to (6). Werefer to the time domain optimised calculation of (1) as theregular method. Firstly, the ambiguity functions resultingfrom the two techniques are compared. Secondly, the effectof extended time processing on the Doppler resolution andintegration gain is considered. The integration gain analysisincludes consideration of target decorrelation effects.Thirdly, experimental results are presented that demonstratethe use of the extended time technique in a prototype802.11 wireless network based PBR operating underrealistic conditions.

Fig. 4 Zero-range cuts of the ambiguity surfaces

3.1 Ambiguity function analysis

The signal used to form the ambiguity surfaces wastransmitted by an 802.11 g wireless access point (Aironet1200 series, Cisco Systems, UK) that normally servicesinternet users in the UCL Department of Electronic &Electrical Engineering. Data from this wireless access pointwere recorded using a NetRAD radar receiver operating inpassive mode [28, 29]. Two sequences of data wererecorded at a sampling rate of 50 MHz. In the first, acontinuous block of data was recorded for a duration of335.5 ms. In the second, the total duration of the datasequence was kept at 335.5 ms however, this time wasrecorded in 256 discrete blocks, with the block repetitionfrequency of 128 Hz. In the second instance, the data werespread over 2 s despite the cumulative signal duration stillbeing 335.5 ms. A sample of continuous data recording isshown in Fig. 2 where the structure of the data can beobserved along with the variations in signal magnitude.Note that there are regular periods for which the signalamplitude is zero giving the sequence a ‘pulse-like’ character.Ambiguity surfaces were calculated from the two data

sequence types by applying the regular processing methodto the first recording and extended time processing to thesecond. The ambiguity surface obtained using the regularprocessing method is shown in Fig. 3a and that for theextended time surface is shown in Fig. 3b. It is clear byinspection that the extended time case has a much finervelocity resolution, as would be expected from the extendedCPI.Zero range cuts from the ambiguity surfaces are compared

in Fig. 4, where the improvement in velocity resolution for theextended time processing is clear. The − 3 dB velocityresolution for the regular processing method was measuredto be 0.27 ms-1 and the extended time processing curve hasa −3 dB width of 0.04 ms−1. The peak sidelobe level of thetwo methods was much the same, approximately −20 dB,however, it was noted that the extended time processinghad a greater number of sidelobes with powers of this

Fig. 2 Example of the signal magnitude for 10 000 μs of 802.11 gwireless signal


order. These extra sidelobes are attributed to the interactionof the intended interruption of the recorded signal and theamplitude modulation of the 802.11 g signal, as reportedin [30].

3.2 Comparison of techniques

A more thorough comparison between the extended time andregular processing techniques has been conducted by using asimulation that emulates the experimental PBR. Complexwhite Gaussian noise was added to a 335.5 ms recording of802.11 WiFi signal such that the SNR was 20 dB, and thissignal was regarded as the reference signal. Thesurveillance signal was formed by summingcircularly-shifted and frequency-shifted copies of therecording before adding complex white Gaussian noise toagain give a 20 dB SNR. The 20 dB SNR was chosen sinceshort range, indoor situations were being considered wherethe signals could be expected to be substantially above thenoise floor. Each shifted version of the original signalrepresented a component of the surveillance signal asdescribed in Section 2.1. The circular shift corresponded tothe desired bistatic range delay and the frequency shift tothe desired Doppler shift. For clarity, only two componentswere included. The first had no shifting and represents theDSI. The second, had shifts to represent a target at 100 mof bistatic range and a Doppler shift of 50 Hz, equivalent to3.1 ms−1 for a pseudo monostatic geometry.To investigate the extended time processing concept, sets

of lower dimension vectors, {sm} and {rm}, were ‘cut’ fromthe simulated reference and surveillance data sequences.The length of the vectors was selected to have a duration of2.68 μs, which, after a cross-correlation, results in a bistaticrange profile of length 400 m. This upper range limit wasselected since indoor situations were being considered and400 m seemed a generous maximum range for suchconditions. The gap between the lower dimension vectorswas specified as a multiple of the vector duration that is agap multiple of 0 resulted in a continuous signal, 1 a gap of2.68 μs, 2 a gap of 5.36 μs and so forth. Thus, as the gap


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increases, the cumulative signal duration reduces from themaximum of 335.5 ms.When comparing the techniques, the total signal duration
between the two methods was held constant, that is bothhad the same CPI and same time-bandwidth product. Thecumulative duration for the extended time processing wasthe duration of the lower dimension vectors multiplied bythe total number of vectors. A section of signal withmatching duration was cut from the start of the simulatedreference and surveillance signals for use in regularprocessing. For example, for 1000 vectors, the cumulativeduration of the extended time method would be 2.68 ms,hence, only the first 2.68 ms of the signals would be usedin the regular processing method. Example range-velocitysurfaces for the two methods are presented in Fig. 5 for thecase when the gap multiple is 5, which gave a signalduration of 55.9 ms. The target location is found by a peakvalue detector and in each case is indicated on the figure bythe white cross. The target was detected in this way at thecorrect range and velocity. Note, however, that the − 3 dBresolutions in the velocity direction are different for the twotechniques, because of the increased observation interval forthe extended time processing.The change in target echo power and velocity resolution as

the gap multiple increases are presented in Fig. 6. In Fig. 6athe target echo powers have been normalised by the zero gapmultiple, that is using the full signal power. At a gap multipleof zero the target power obtained by the two techniques is thesame. As the gap multiple increases, the total signal durationshortens, resulting in lower target echo power consistent withequivalent changes in coherent integration time. Thus, theshape of the curve in Fig. 6a is a good approximation tothe curve that would be obtained plotting 20 log10(Tsig),where TSig is the cumulative signal duration.

Fig. 5 Comparison of regular processing

a And extended time processingb Using simulated data

Fig. 6 Comparison of

a Normalised target power andb Velocity resolution for the two techniques


Conversely, the velocity resolutions resulting fromapplication of the two techniques, are quite different asshown in Fig 6b. As the gap multiple increases, the regularprocessing resolution degrades in a linear fashion whereasfor the extended time processing it remains constant. Fromthe bistatic radar theory [1, 2] the − 3 dB resolution can beexpressed as

Dv3 dB = 1/Tobs( )

l/{2 cos (b/2)} (8)

where Tobs is the observation interval, λ is the carrierwavelength and β the bistatic angle. (Note (8) is essentiallya rearrangement of (2).) In the simulation, for the extendedtime processing Tobs remained constant, at 335.5 ms as thegap multiple increased, whereas for regular processing Tobsis equal to Tsig and decreased as the gaps multipleincreased. It is clear from (8), that extended time processingimproves velocity resolution and, to first order, maintainsdetection performance.The limit on the observation interval will be a strong

function of target decorrelation [2]. Real world targetsignatures can vary rapidly because of scintillation andmoving components. Moving components tend to exhibitvelocities different to the gross velocity, for example, theswinging of the limbs of a human target. Thesemicro-motions create variation in the phase of the returnsignal, which contribute to the time over which coherentintegration can be efficiently applied. In other words, ifintegration is performed for longer than the decorrelationtime, the integration gain will fall and the power of thesignal received from the target will be reduced despite theincrease in time-bandwidth product. Since the extendedtime processing observes the target for longer periods, it ispotentially more prone to target decorrelation.To probe this further, the target signal was subjected to

decorrelation by introducing a phase error to every sampleof the shifted signal representing the target response. Theerror was modelled as ej2πγ where γ was a draw from auniform probability distribution. γ was not varied for everysample of the signal, instead it was changed after aspecified time referred to as the ‘phase error interval’. Thisinterval represented the duration over which the targetresponse remained constant. The variation in normalisedtarget power, in the range velocity surface, is shown as afunction of the phase error interval for the two processmethods in Fig. 7. Ten Monte Carlo trials were used, andthe averaged results are presented. In the example shown inFig. 7, the gap multiple was set to 5, and hence thecumulative signal duration was 55.9 ms. The phase errorinterval was reduced from 400 ms to 10 ms in 10 ms steps.The figure shows that as the phase error interval decreased,

so too did the normalised target echo power. This is the case,of course, for both techniques, and is caused by the increase

Fig. 7 Reduction in normalised power with increased frequency ofphase error change


Fig. 9 Comparison of range-velocity surfaces for a slow movinghuman target

a Regular andb Extended time processing

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in target decorrelation affecting the efficiency of integrationgain.The normalised target power for regular processing
remains constant until the gap interval falls to 50 ms, whichis shorter than the total signal duration. While the errorinterval was above 50 ms the regular technique observed nomore than one phase change, so the entirety of the signalwas efficiently integrated. Conversely, for the extended timeprocessing, as soon as the phase error interval falls below335.5 ms, phase change errors become significant and thusthe integration gain reduces. Fig. 7 illustrates that targetdecorrelation times will place an upper limit on the totalduration that can be efficiently exploited when usingextended time processing. Target decorrelation will be afunction of the illuminator, the target and the clutterenvironment.In the above simulation, the maximum observation interval

possible was limited to 335.5 ms, a function of the amount ofcontinuous data that could be recorded with the availableequipment. For a real target, the decorrelation time can bemuch longer. In the next section, we examine targetdecorrelation using the extended time processing techniquewith an 802.11 g wireless network based PBR.

3.3 Extended time processing experiment

The PBR described above was configured to record echosignals from human subjects moving at approximatelyconstant velocity along a corridor in the UCL Departmentof Electronic & Electrical Engineering. The geometry of theexperiments is shown in Fig. 8 and the pertinent systemparameters are listed in Table 1.There was no direct line of sight between the access point

and the target therefore all measurements werethrough-the-wall. The reference and surveillance nodes werespatially separated rather than co-located. The surveillancechannel configuration was of the ‘reverse over the shoulder’

Fig. 8 Experimental test range

Table 1 Operating parameters for the PBR

Parameter Value

WiFi AP channel 8 (2.447 GHz)NetRAD oscillator freq. 2.425 GHzsurveillance antenna 10°/24 dBireference antenna 30°/15 dBireference channel attenuation 10 dBtotal record duration 335.5 mssample freq 50 MSamp/sextended time repetition rate* 128 Hznum extended time block* 256


type. This means that the illuminator is in the antennamainlobe, a 10 dB attenuator was placed between theantenna and the receiver hardware in the surveillancechannel to prevent ADC saturation. For the referencechannel, a lower gain antenna was employed.The target was a human walking along a corridor towards

the surveillance channel receiver. The data recording wasmade when the volunteer was at the location indicated inFig. 8, where the bistatic range was approximately 32.5 mand the bistatic angle 5°. The volunteer was asked to walkslowly, so that their Doppler shift would be small and closeto that of the stationary clutter. Two experiments arereported here. For the first, a continuous recording wasmade, enabling processing with the regular method. For thesecond, an interrupted recording was made followingprocessing with the extended time method. As indicated bythe parameters in Table 1, the total observation interval forthe extended time method was 256/(128 Hz) = 2s.Range-velocity surfaces for the two processing methods are

presented in Fig. 9. The CLEAN processing techniquedescribed in [3] was used to remove the DSI and otherstationary clutter responses. The velocity scales on they-axis include allowance for the bistatic angle. Whitecrosses indicate the target locations, which were found bypeak power detection under the assumption of a singletarget. For regular processing the non normalised targetpower was 74.9 dB and the bistatic range and velocity were30 m and 0.63 ms−1; for the extended processing the valueswere 74.2 dB, 24 m and 0.66 ms−1 respectively. However,in line with the analysis of Section 3.2 the − 3 dB width ofthe peak in the velocity direction was 0.26 ms−1 for regularprocessing but only 0.07 ms−1 for extended timeprocessing. Thus, for the WiFi access point illuminators, theextended time processing technique can be applied at leastover a total duration of 2 s without sacrificing overallsystem sensitivity.

4 Summary & conclusion

In this paper, we have introduced extended time processing asa method that provides for a more flexible way of processingPBR data. This new method allows increased targetobservation times without increasing the computationaloverhead and without sacrificing system sensitivity (andthus detection range) compared with regular processingmethods. The technique works by interrupting the recordingof the reference and surveillance channels so that shortsections of signal of opportunity are digitised at regular


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intervals. In extended time processing, the time-bandwidthproduct that would be achieved with regular processing isspread, in short bursts, over an extended target observationinterval. Since the total time-bandwidth product remains thesame, the computational overhead required for processingdoes not increase, however, improved velocity resolution isobtained from the longer observation interval. Thus, thesystem is able to resolve multiple targets from each other inthe Doppler domain and discriminate slow moving targetsfrom stationary clutter in a better manner. Suchimprovements in Doppler performance are valuable sincethe range resolutions of PBR are typically lower than theirmonostatic counterparts because of bistatic geometry [1].The limit to the target observation interval was shown to
relate to the target decorrelation time. With its increasedobservation times, the extended time processing was shownto be more susceptible to decorrelation difficulties thanregular processing. The observation interval must be keptshorter than the target decorrelation time to prevent lossesin integration gain. However, it is important to note thatdecorrelation time is a function of the target and not theprocessing technique. A practical example was provided inwhich a human target was observed using an 802.11wireless network based PBR. An observation interval of 2 swas obtained by the extended time processing, despitethe cumulative signal duration for the system being only335.5 ms.In the future, the authors intend to investigate the role of

extended time processing for target separation in indoorenvironments. The 802.11 g wireless network signalworking bandwidth is 16 MHz, giving a monostatic rangeresolution of approximately 18 m that is too coarse fordiscriminating indoor targets. However, the measured targetvelocities depend on bistatic angle [1, 2], which variesrapidly for the short target ranges that occur in indoorsituations. Through the use of extended time processing, thefine velocity resolution achievable may allow the targets tobe separated based on these velocity variations rather thantheir bistatic ranges.

5 Acknowledgments

The authors thank Dr. Hui Guo for her assistance during theexperiments.

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