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Hindawi Publishing CorporationInternational Journal of Aerospace EngineeringVolume 2013, Article ID 521630, 12 pageshttp://dx.doi.org/10.1155/2013/521630

Research ArticleTracking of Noncooperative Airborne Targets UsingADS-B Signal and Radar Sensing

Ming-Shih Huang,1 Ram M. Narayanan,1 Yan Zhang,2 and Arthur Feinberg3

1 Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA2 School of Electrical & Computer Engineering, University of Oklahoma, Norman, OK 73109, USA3 Intelligent Automation, Inc., Rockville, MD 20855, USA

Correspondence should be addressed to RamM. Narayanan; [email protected]

Received 31 October 2012; Revised 2 January 2013; Accepted 6 January 2013

Academic Editor: Srinivas R. Vadali

Copyright © 2013 Ming-Shih Huang et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

As the Automatic Dependent Surveillance-Broadcast (ADS-B) system has gained wide acceptance, additional exploitations ofthe radioed satellite-based information are topics of current interest. One such opportunity includes the augmentation of thecommunication ADS-B signal with a random biphase modulation for concurrent use as a radar signal. This paper addresses theformulation and analysis of a suitable noncooperative multitarget trackingmethod for the ADS-B radar system using radar rangingtechniques and particle filter algorithms. In addition, the low-update-rate measurement due to the ADS-B system specification isdiscussed in order to provide acceptable estimation results. Simulation results show satisfactory tracking capability up to severalkilometers with acceptable accuracy.

1. Introduction

During the past half century, air traffic management (ATM)has relied heavily on centralized ground stations usingprimary surveillance radar (PSR) and secondary surveillanceradar (SSR) systems. With dramatic increase in the numberof aircraft in the future, PSR and SSR will soon reach theirlimitations in their capability to monitor aerospace trafficpatterns and aircraft locations in a timely manner to avoidcollisions. Several decentralized approaches have drawnmoreattention lately thanks to their potential for quicker responseand self-reliance. Independent of air traffic control (ATC),the widely used traffic alert and collision avoidance system(TCAS) monitors the airspace around an aircraft and com-municates with other aircraft equipped with a correspond-ing transponder. TCAS is effective and successful in Class A,B, and C airspaces and Class E airspace above 10,000 feet,since aircraft in this airspace are required to be equippedwith altitude-reporting transponders. However, TCAS isineffective for noncooperative aircraft and also in low-altitude Class E and uncontrolled airspace. To enable thetransformation of the ATM to a new paradigm that can

meet the demand for the next 20 years and beyond, sev-eral developmental programs are underway, such as SingleEuropean Sky Air traffic Research System (SESAR) in Europe[1], Next Generation Air Transportation System (NextGen)in U.S.A. [2], and Collaborative Actions for Renovation ofAir Traffic Systems (CARATS) in Japan [3]. The philosophyis to move away from legacy ground based technologies toa new and more dynamic satellite based technology. A keyelement of SESAR and NextGen is ADS-B, which uses theGlobal Navigation Satellite System (GNSS) signals to pro-vide air traffic controllers and pilots with precise positioninformation in space, in contrast to the traditional surveill-ance radar derived data [4]. Aircraft transponders receiveGNSS signals and use them to determine the aircraft’s pre-cise location in the sky, which is combined with other relev-ant data and broadcast to other aircraft and air traffic con-trol facilities via a digital data link. Besides ADS-B’s wideacceptance in Europe and USA, NAVCANADA commencedoperational application of ADS-B as a means of providingaircraft surveillance information to Air Traffic Controllers(ATC) [5], and AirServices Australia commissioned theADS-B Upper Airspace Project (UAP), providing ADS-B

2 International Journal of Aerospace Engineering

coverage across the whole continent [6]. In addition, ADS-Bis being used as the key solution for UnmannedAerial System(UAS) integration in the National Airspace (NAS). Whenproperly equipped with ADS-B, both pilots and controllerswill see the same real-time displays of air traffic, substan-tially improving safety and minimizing collision probabil-ity.

However, before the ADS-B implementation and oper-ation are fully complete, there will be a transition periodinvolving coexistence of ADS-B equipped and nonequippedaircraft. Similar to TCAS, the ADS-B system works only forcooperative aircraft equipped with a corresponding systemand is, thus, blind to noncooperative targets that are notsimilarly equipped. It has been pointed out that the limiteduse of ADS-B as the sole means of surveillance may lead toa reduction of the integrity of the entire ATC system [7, 8].Furthermore, localized problems, such as lack of connectivityto the required four visible satellites, will confuse not onlyaircraft pilots but also ATC [9, 10]. Hence, it is desired to finda way to cope with the noncooperative targets while retainingthe benefits of the ADS-B system.

Since the emergence of ADS-B concept, some researchershave considered the utilization of existing and installedinfrastructure of the surveillance radar to combine with thesatellite-based ADS-B system within the perspective of ATC.More interestingly, the use of the ADS-B signal itself todetect noncooperative targets from the ADS-B message andfrom the radar processing, as an onboard collision avoidancesystem, was first described in a patent disclosure [11], andthe concept subsequently developed further [12–15]. Thenovelty of the ADS-B radar system lies in that the systeminsightfully exploits the ADS-B out signal, which is primarilydesigned for communication purposes, as a radar signal toperform multiple target estimation and tracking, therebycreating a multifunctional waveform. However, details ofsuch radar-communication system specifications and prob-lems of low-update-rate measurement due to the ADS-Bsystem requirement have not been fully discussed.This paperprovides details of the ADS-B radar system design, includingthe link power budget, and RF signal interference analysis.Furthermore, multitarget tracking using particle filter forthe ADS-B radar system is proposed. In addition, the slowconvergence in particle filter algorithm and themeasurementat low-update-rate, owing to the ADS-B signal broadcastrate of once per second, is addressed. It is important tonote that in the ADS-B radar system, the radar capabilityis independent of the ADS-B message data integrity. There-fore, the coexistence of ADS-B and radar sensing achievesincreased reliability and redundancy, because it becomespossible to identify malfunctioning sensors and removesfalse alarms through performing conformance checks bycomparing communication and radar reports.

The remainder of the paper is organized as follows.Section 2 briefly reviews the evolution of air surveillance sys-tems. The ADS-B radar system architecture and link budgetare provided in Section 3. Then, the multitarget estimationalgorithm for ADS-B radar system is proposed and dis-cussed in Section 4. Computer simulations that demonstratethe functionality of ADS-B radar system for multitarget

estimation performance are provided in Section 5. Conclu-sions are drawn in Section 6.

2. Evolution of Surveillance Technologies

For the sake of completeness and continuity, a brief andcomparative review of the existing and new surveillancetechnologies is provided in this section.Themajor advantagesand disadvantages for each approach are pointed out withan emphasis on the noncooperative surveillance capability.In addition, it can also be seen through the transition ofthe surveillance technologies that ATM is shifting fromcentralized systems to decentralized systems as the density ofthe air traffic continues to increase.

2.1. PSR and SSR. For the past few decades, PSR and SSRhave been the main two components of an ATC station. PSRhas the capability to detect large metal objects, includingcooperative and noncooperative targets, while SSR worksonly for transponder-equipped aircraft. SSR relies on aircraftwith corresponding transponder, but the report providesaircraft identification. Both PSR and SSR were designed forlow and medium traffic situations. Due to the insufficientrange ambiguity, they are unable tomeet the challenge of hightraffic volume. Moreover, ground-based surveillance radarhas limitations at low altitudes and may be influenced byatmospheric conditions.

2.2. TCAS. Due to continuing growth in air traffic, TCAS orother similar devices have been in various stages of researchand development since the early-to mid-1950s to serve asa last resort collision avoidance safety net. TCAS operatessimilarly to the ground-based SSR but independently interro-gates surrounding aircraft on a 1030-MHz radio channel.Thepilot will be alerted to the presence of the intruding aircraftreplying to the interrogation via 1090-MHz radio frequency.Current generation TCAS II, jointly developed by the USRadioTechnical Commission forAeronautics (DO-185B) andEuropean Organization for Civil Aviation Equipment (ED-143), issues two types of advisories: the resolution advisory(RA), which identifies an intruder that is considered acollision threat, and the traffic advisory (TA), which identifiesan intruder that may soon cause an RA. According to thepredicted closest point of approach (CPA), TCAS producesa TA at approximately 45 seconds and an RA at 35 secondsto CPA. TCAS is designed to reduce the incidence of mid-aircollisions and has been very successful since its introduction.However, the major concern with regard to either TCAS orADS-B is that they are not required all the time, and ADS-Bout is not yet mandated in most countries. Aircraft equippedwith TCAS and/or ADS-B are still exposed to danger ofcollisions in low altitude of Class E and uncontrolled airspaceowing to their inability to detect noncooperative targets andunawareness of any illegal intruder in transponder-requiredairspace.

2.3. ADS-B. ADS-B is redefining the paradigm of commu-nication, navigation, and surveillance in ATM. An ADS-Bequipped aircraft determines its own position using GNSS

International Journal of Aerospace Engineering 3

ADS-B out (Blind to ADS-B)

ADS-B out

(a)

Detectable throughreflected microwave

ADS-B out

ADS-B out + reflection

(b)

Figure 1: Surveillance principles and concepts for ADS-B and ADS-B radar. (a) ADS-B: communication between equipped aircraft and ATC.(b) ADS-B radar: able to detect both equipped and nonequipped aircraft.

and periodically broadcasts its four-dimensional position(latitude, longitude, altitude, and time), track and groundspeed, aircraft or vehicle identification, and other additionalrelevant data as appropriate, for example, intended trajecto-ries [16], to nearby aircraft also equipped with the ADS-Bsystem and potential ground stations without expectation ofan acknowledgement or reply. One of the most significantadvantages of the ADS-B system is that it minimizes radiofrequency (RF) spectral congestion as would be generatedby TCAS. Any user, either aircraft or ground stationswithin broadcasting range, may receive and process ADS-Bsurveillance information. ADS-B system provides accurateinformation and improves situational awareness. Moreover,ADS-B enables a shift from a centralized, ground-basedATM system to a decentralized network involving pilots andaeronautical operational control centers. It might eventuallyallow pilots to use onboard instruments and electronics tomaintain a safe separation and to reduce their reliance onground controllers.

2.4. ADS-B Radar. While the ADS-B system can only beconsidered as a decentralized cooperative surveillance ATMtechnique, the ADS-B radar system deals with both coopera-tive and noncooperative targets. Figure 1 depicts the generalworking principle for ADS-B radar concept, as well as thecomparison between the ADS-B system and the ADS-Bradar system. Noncooperative targets, such as unmannedaerial vehicles (UAVs) and private jets, are blind to theADS-B systems when not equipped with the ADS-B system,but they pose equal collision danger if not being detected.Moreover, an inexpensive backup surveillance is necessary inthe case of lost, incorrect, jammed, or compromised satelliteinformation possibly caused by localized problem or devicemalfunction. The ADS-B radar system is introduced as aninsightful modification to the standard ADS-B system inthe interests of a safe backup service without significantlyenlarging the volume of ADS-B equipment. Since the ADS-B systems broadcast signals periodically, the reflective ADS-B electromagnetic energy could be viewed and extended foruse as radar echoes. The returned echoes will be exploited todetect and track targets, including noncooperative ones, andhence, the pilot can be alerted without relying on guidancefrom the ground station to maintain aviation safety. Further-more, by comparing the communication and radar reports

RFcoherent

transceiver

GPS ADS-Bcodec

CDTI

Multiplereceiving antennas

StandardADS-B transceiver

Radarprocessing

Figure 2: Conceptual architecture of ADS-B radar system.

for the ADS-B equipped targets, the ADS-B radar system cansuppress the creation of tracks based onADS-Bmessages thatcontain intentionally incorrect position information.

3. System Design Specification

A conceptual architecture of the ADS-B radar system isdepicted in Figure 2. This differs from the correspondingfigure in Zhang and Qiao [11] by pointing out the possibilityto fuse the information from the radar processing and theADS-B message, as well as the target estimation method inradar processing. The fundamental principle of the ADS-Bradar system is to use reflective ADS-B signals with only fouradditional L-band antennas on the standard ADS-B systemto perform multitarget estimation and tracking for bothcooperative and noncooperative targets. Because the ADS-B receivers only respond to the envelope of the incomingsignal, phase modulation will not affect the normal ADS-Bsignal decoding for other aircraft. As a result, the standardADS-B system will still be able to correctly decode the phase-modulated signal to obtain the communication message.TheADS-B radar system retains the communication capabilityand receives the standard ADS-Bmessages from neighboringaircraft, just as the standard ADS-B system does. Onceper second, it also broadcasts the phase modulated ADS-B signal. After sending out the electromagnetic wave, thereceiving antennas capture the returned signal reflectedfrom surrounding metallic objects, and cross-correlation isperformed between the transmitted burst and the returnedechoes. Both the ADS-B In information and the estimatedlocation from the radar report can be fed intoCockpitDisplayof Traffic Information (CDTI) [17].


0 1 2 3 4 5 6−1.5

−1

−0.5

0

0.5

1

1.5

Number of bits

Pulse

ampl

itude

Illustration of random biphase modulation

ADS-B signalADS-B radar signal

Figure 3: Illustration of the ADS-B waveform (blue) versus theADS-B radar waveform (red).

The design consideration is focused on the feasibility ofutilizing the returnedADS-B signal as a radar echo in terms ofthe signal-to-noise ratio and radar performance. In addition,the 500W peak transmit power, the 120𝜇s pulse durationspecifications of the 1090MHz ADS-B standard, and thebroadcasting rate impose constraints on the system design,requiring a detailed analysis.

3.1. Signal Waveform and Interference Analysis. Each of theADS-B Extended Squitter is 120𝜇s long, including 8𝜇spreamble and 112 𝜇s data block, and is similar to TCASmessage format, except for longer 56 𝜇s ADS information.Besides, the ADS-B message is digitally encoded using pulseposition modulation (PPM). When a communication signalis utilized and treated as a radar signal, many issues need tobe addressed. First, the signal must be suitably modulatedfor performing matched filtering in the receiver. The productof the PPM signal waveforms for two different bits can onlybe either zero or positive, which translates into a raisedcorrelation noise floor. Second, the constraints of pulse dura-tion, peak transmit power, signal bandwidth, and coherentreturned signals degrade the radar performance. Therefore,the primary task for the ADS-B radar design is to exam thefeasibility of treating the ADS-B signal as a radar signal.

In order to improve the radar performance, the majormodification to the ADS-B signal is random biphase mod-ulation introduced to each bit. Figure 3 shows the simulatedADS-B waveform and the proposed ADS-B radar waveform.Both signals are of the same duration and have identicaldigital messages, except that the ADS-B radar signal hasa random 180∘ phase shift. By randomizing the transmitsignal, the matched filtering operation can be performedby cross-correlating the reflected signal with a time-delayedreplica of the transmit waveform. The biphase modulationrenders both positive and negative products, forcing the

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1×10−5

−0.1

00.10.20.30.40.50.60.70.80.9

Time (s)

Auto

corr

elat

ion

ADS-B signalADS-B radar signal

Figure 4: Autocorrelation of standard ADS-B signal and autocorre-lation of the ADS-B radar signal.

−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1×10−5Time (s)

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2Interference

With another standard ADS-B signalWith another ADS-B radar signal

Cros

s-co

rrel

atio

n

Figure 5: Cross-correlation of a standard phase-modulated ADS-B signal with another standard ADS-B signal and another phase-modulated ADS-B radar signal.

autocorrelation to be statistically zero except for zero time-lag. The effect of the sum of the products in the autocor-relation function is provided in Table 1. The significance ofthe random biphase modulation (0∘ or 180∘) can be seenclearly in the autocorrelation of the ADS-B radar signal andthe ADS-B signal, as depicted in Figure 4. It is clear that theautocorrelation of the ADS-B radar signal outperforms thatof the original modulation-free ADS-B signal and shows animproved correlation peak-to-noise ratio.

Since it is very likely to have other ADS-B signals inthe same channel, it is critical to analyze the interference ofthe ADS-B signals and possibly other ADS-B radar signalsas well. Figure 5 shows that a typical ADS-B radar signal isuncorrelated with both a standard ADS-B signal as well asanother independent ADS-B radar signal, thereby indicatingthat the on-board ADS-B radar receiver will not be affectedby standardADS-B orADS-B radar transmissions fromotheraircraft in the vicinity.


Table 1: Effects of random biphase modulation on correlationresults.

Messages

Modulation

PPMPPM with random biphase PMSame phase

(no phase shift)Inverted phase(180∘ phase shift)

(1, 1) or (0, 0) Positivesum-product

Positivesum-product

Negativesum-product

(1, 0) or (0, 1) Zerosum-product

Zerosum-product

Zerosum-product

Because the modulated signal has wider bandwidth (theresultant waveform still complies with the ADS-B standard),the range resolution is improved and the range side lobes arealso further suppressed [18].The range resolution of 75metersfor the ADS-B radar system is sufficient for most collisionavoidance applications.

3.2. System Configuration and Link Budget Analysis. TheADS-B radar system is composed of the following compo-nents: (a) ADS-B transceiver, (b) ADS-B encoder/decoder,(c) radio frequency (RF) electronics with up- and down-frequency conversion, crosstalk cancellation, and filteringcapabilities, (d) a phase modulator, and (e) four additionalantennas operating at 1090MHz. In particular, (d) and (e)are additional to the original ADS-B system to process thereflected radar signals and to estimate target locations.

The link power budget is analyzed based on the radarrange equation as follows:

𝑃𝑟=𝑃𝑡𝐺𝑡𝐺𝑟𝜆2

𝜎

(4𝜋)3

𝑅4, (1)

where 𝑃𝑟and 𝑃

𝑡are the received and transmitted power,

respectively; 𝐺𝑡and 𝐺

𝑟are the receiving and transmitting

antennas gain, respectively; 𝜆 is the signal wavelength; 𝜎 isthe target’s radar cross-section (RCS); 𝑅 is the range to thetarget. To improve range resolution, common radar signalpulse durations are as short as tens of nanoseconds. However,since the signal energy is emitted continuously, integrationgain can be achieved by utilizing the 545 sinusoidal cycles ineach bit. As shown in Table 2, for a target having an RCS of0 dBsm (1 square meter) at a distance of 4.5 km, the signal-to-noise ratio (SNR) is 5.0 dB, a desirable value. For a largeairliner with a higher RCS of 20 dBsm (100 square meters),the operational range for the same SNR value could extend to14 km, which allows more than one minute of reaction time.

4. Multitarget Estimation andTracking Algorithm

The Multiple Signal Classification (MUSIC) technique [19]was initially employed for the ADS-B radar to performdetection and estimation of single target angle-of-arrival(AOA). Due to the nature of the ADS-B radar signal, thereare several important factors that limit the extension to mul-titarget estimation, primarily (a) the number of detectable

targets and (b) signal coherency. The MUSIC algorithm hasthe well-known inherent constraint that the number of thereceivers has to be larger than that of the targets. With fixednumber of 𝑁 antennas, the maximum number of detectabletargets using the MUSIC algorithm is 𝑁 − 1. While it isdesirable to minimize the cost and amount of antennas usedin the ADS-B radar system, the detection capability becomesstrictly limited. To further elaborate the signal coherencyproblem, the returned signals bouncing off from differenttargets are highly correlated because all the reflected radarechoes are simply scaled replicas of the transmitted signalbut slightly frequency shifted by the Doppler shift, whichis about 1 kHz for the relative speed of 150m/s. Althoughthere has been research dealing with correlated signals, suchas constrained MUSIC [20], cumulant-based coherent signalsubspace method [21], and focusing matrix for coherentsignal subspace processing (for wide-band signals) [22], theseapproaches do not apply to the scenario that the ADS-Bradar system encounters owing to either unavailable priorinformation or limited signal bandwidth from the ADS-Bsignal specification. Other methods, like the least squaresand the unscented Kalman filter approaches, require priorinformation of the motion model. A hybrid approach thatcombines the time-of-arrival (TOA) and the particle filterapproaches for multitarget estimation and tracking for theADS-B radar system is proposed in this paper. In the particlefilter algorithm, the unavailable target motion can be dealtwith using the concept of jittering [23], by introducing anoise model to cover the uncertainty of the target location.After the radar report is obtained, multisensor data fusion isa discrete problem, and attempts to combineADS-Bmessageswith radar reports are described in [7, 8].

4.1. Multitarget Detection and Ranging through TOA. Let𝑠(𝑡) represent the transmitted signal. The distance betweenthe target and the own aircraft can be obtained by cross-correlating the delayed transmitted signal, 𝑠(𝑡 − 𝜏

𝑑), with the

received signal, 𝑠(𝑡 − 𝜏𝑟), where 𝜏

𝑑is the internal delay and

𝜏𝑟= 2𝑅/𝑐 is the round-trip time to the target. The target

range is 𝑅, and the speed of light is 𝑐. In theory, the cross-correlation peak occurs when 𝜏

𝑑= 𝜏𝑟, from which the range

can be determined. In our simulation experiment, the peaklocation may vary a little due to the measurement noise andphase coding scheme. In the ADS-B radar system, 𝑠(𝑡) isthe phase-modulated ADS-B signal. By continuously cross-correlating the received signal with the delayed transmittedsignal replicas, the number of peaks above a predeterminedthreshold indicates the number of detected targets.Moreover,the ranges can be calculated based on the delay times asdescribed above. To correctly associate the ranges with thecorrect targets can be a difficult problem [24, 25]. However,because the sensors are located only several meters apart, thereturned echoes from different targets usually arrive at thesensors in the same sequence.

4.2. Multitarget Estimation. The particle filter algorithmis briefly reviewed in this section. In addition, a simplemultitarget estimation method based on particle filter isproposed for the application of the ADS-B radar system.


Table 2: Link budget analysis.

Receiver noise floor = −110.9 dBm Received signal power = −105.9 dBmNoise figure 3 dB Peak transmit power +57 dBm

Bandwidth 1MHz baseband Processing gain(545 samples coherently integrated) 27.4 dB

Antenna gain(omnidirectional) 0 dB

Assumed RCS 0 dBsm1/(4𝜋)3 −33.0 dB

1/(Range)4 (at 4.5 km) −146.1 dBm−4

Square of wavelength −11.2 dBsmSignal-to-noise ratio, SNR = 5.0 dB

Furthermore, the problem of slow convergence in particlefilter and measurements at low-update-rate is addressed.

4.2.1. Particle Filter Algorithm. Particle filter [26–28], alsoknown as sequential Monte Carlo method, bootstrap fil-tering [29], and the condensation algorithm in computervision [30], is very suitable for nonlinear and nonGaussianestimation and tracking applications as encountered in thereal world. A particle filter is essentially composed of threestages: prediction, update, and resampling. The predictionstage uses the system model to predict the state probabilitydensity function (PDF) forward from one measurement timeto the next. Since the state is usually subject to unknowndisturbances, prediction generally spreads the state PDF.Theupdate operation takes the latest measurement to modifythe prediction PDF using Bayes’ formula. A resampling stepwas introduced by Gordon et al. [31] in order to discardthe particles with very low weights. Particle filters work byproviding a Monte Carlo approximation to the PDF whichcan be easily updated to incorporate new information as itarrives.

A discrete time estimation problem is considered here.The state vector is denoted by x

𝑡whose temporal evolution

is given by the state equation:

x𝑡= f(x𝑡−1) + v𝑡−1, (2)

where f is the state transition function, and v𝑡is the process

noise with zero mean but not necessarily having a Gaussiandistribution. In the ADS-B radar system, the components ofthe state vector will be target locations. At each discrete timepoint an observation y

𝑡, related to the state vector, can be

represented as follows:

y𝑡= h(x

𝑡) + n𝑡, (3)

where h is the measurement function and n𝑡is the mea-

surement noise with zero mean but not necessarily havinga Gaussian distribution. n

𝑡is assumed to be uncorrelated

with v𝑡. In our application, y

𝑡is the range information and

h is the process to obtain the target ranges through the TOAtechnique.

A numerical integration method for the particle filteralgorithm is listed below.

(1) Particle generation: within the field of view(FOV), create 𝑁 particles and associated weights(x𝑛𝑡−1

, 𝑤(x𝑛𝑡−1

))𝑛=1,...,𝑁

according to the uniformdistribution.

(2) Prediction: particles propagate according to evolutionequation (1).

(3) Measurement update: use the available measurementsto compute the likelihood of each particle and updatethe weights of all particles with 𝑎 posteriori density.This is accomplished using

𝑤(x𝑖𝑡

) ∝ 𝑤(x𝑖𝑡−1

) 𝑝 (y𝑡| x𝑖𝑡

) , (4)

where the final weights sum to one, namely,∑𝑁

𝑖=1

𝑤(x𝑖𝑡

) = 1.(4) Systematic resampling: after a predetermined number

of iterations, take 𝑁 samples with replacement fromthe current particle set based on 𝑤(x𝑖

𝑡

).(5) Iteration: letting 𝑡 = 𝑡 + 1, repeat the process until

desired estimation error is achieved.

For abruptly changing systems, Interacting MultipleModel (IMM) method [32–34] and Generalized Pseudo-Bayesian (GPB) [35] are widely used in the target trackingliterature. To reduce the computational complexity on theADS-B radar system, these approaches are not adopted. Infact, we do not even use the motion equation, since thedynamic model of the object cannot be obtained. In the pre-diction step, particles are simply scattered with a large vari-ance that is able to cover abrupt motion changes.

Assume there are two targets around the own aircraftequipped with ADS-B radar system. Each particle will beassigned two weights according to the state PDF. In theresampling step, these two weights will be merged by takingthe largest weight for each particle. This process has two-foldimplication: (a) the peak selection from the posterior dis-tribution and (b) partition of the particles to represent eachtarget. The index of the maximum value out of the twoweights for one particle will be used tomark which target thisparticle is associated with and is more likely to be close to.Since no two targets should and will share the same location,the larger weight of the partitioned particle is significant


Sum to 1

Sum to 1

Sum to 1

𝑟

· · ·

· · ·

· · ·

· · ·

𝑤new

𝑁−1𝑁−1

max(𝑤𝑖𝑘)

𝑤1(𝒙𝑖𝑡)

𝑤2(𝒙𝑖𝑡)

𝑤1(𝒙2𝑡 ) 𝑤1(𝒙

3𝑡 )

𝑤1(𝒙3𝑡 )𝑤1(𝒙

1𝑡 )

𝑤1(𝒙1𝑡 )

𝑤2(𝒙1𝑡 ) 𝑤2(𝒙

2𝑡 ) 𝑤2(𝒙

3𝑡 )

𝑤2(𝒙2𝑡 )

Figure 6: Resampling mechanism for multiple targets.

8 8.5 9 9.5 10 10.5 11−1.5

−1

−0.5

0

0.5

1

1.5ADS-B radar waveform

Time (s)

Pulse

ampl

itude

×10−6

Figure 7: Transmitted ADS-B radar signal waveform.

and retained while the smaller weight is discarded. Afterthe resampling procedure, all particles whose both weightsare low will be discarded and only those particles with atleast one high weight will survive. Moreover, parts of theparticles will be representing Target 1 if their first weightsare clearly larger than the second ones. Figure 6 depicts theidea of such resampling mechanism for multiple targets. Theresampling step removes particles that are improbable tobe any of the targets, but it requires additional processinglatency. Hence, we would like to intelligently pick up theright time to do the resampling step. The parameter𝑁eff, theeffective sample size, can be used to measure the degeneracyof the algorithm [36, 37]. Once the particles spread out allover the place and only a certain amount of particles aremeaningful, 𝑁eff will become small. This is the right timeto spend extracomputational expense to discard low weightparticles. A good estimate of the effective sample size is givenby �̃�eff = [1 + 𝑁

−1

∑𝑁

𝑖=1

[𝑁 × 𝑤(x𝑖𝑡

) − 1]2

]−1

, which rangesfrom 100% to 0%.The resampling step is performed when thenumber of meaningful particles is less than a predeterminedthreshold value, 𝑁threshold. This enables the particle filteralgorithm to simply do the resampling at appropriate times.

The numerical approximation of adaptive resampling for 𝑚targets is given as follows, if𝑚 is known.

The procedure is described below.

(1) Set up𝑁threshold, which represents the number of themeaningful particles, to be within [0, 1].

(2) Derive 𝑤new = {max (w𝑖𝑘

) |𝑘=1,...,𝑚

, 𝑖 = 1, . . . , 𝑁}.

(3) Calculate �̃�eff = [1 + 𝑁−1

∑𝑁

𝑖=1

[𝑁 × w𝑖𝑘

− 1]2

]−1

, 𝑘 =

1, . . . , 𝑚.(4) Ifmin{�̃�eff(𝑤1), . . . , �̃�eff(𝑤𝑚)} < 𝑁threshold, then take𝑁 samples with replacement from the current particleset using 𝑤new.

4.2.2. Supplementary Particle Filter Algorithm. As the ADS-B radar measurement is available once per second and thePF can be completed in the order of one-tenth of a second,most of the time the system is idling and waiting for the newmeasurement. To improve the estimation performance, somesort of upsampling process is desirable and can be realizedthrough piecewise constant interpolation between successivemeasurements. We name the algorithm using interpolatedmeasurement on PF as the supplementary particle filter(SPF). Before the new measurement arrives, SPF performsiteratively using the current observation as if the target werestatic. Standard PF is vulnerable to sample impoverishment,because a finite number of particles are used to approximatea continuous distribution. The benefit of SPF over standardPF is that SPF minimizes the estimation error when thesample size is not sufficiently large. SPF provides improvedestimation accuracy because particles will be redistributedto high likelihood areas over iterations although the samemeasurement information is reused.The sample resolution isessentially enhanced during SPF iterations, thereby improv-ing the estimation performance.

SPF not only improves the estimation accuracy betweensuccessive measurements but also benefits further the esti-mation result when new measurement arrives, because moreparticles are already allocated to local mode of the posteriordensity. Through extensive simulation analysis, it has beennoticed that PF takes many iterations to converge. Whilethe ADS-B message is available only once a second, a fewdozen seconds may be needed to obtain accurate estimated


0.2 0.4 0.6 0.8 1 1.2×10−4

×10−5

×10−5

×10−5

×10−5

×10−18

0−1

01

Transmitted signal

6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6

6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6

6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6

6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6

−2

02

Received signal from sensor 1


Pulse

ampl

itude

Pulse

ampl

itude

×10−18

−2

02

Pulse

ampl

itude

×10−18

−2

02

Pulse

ampl

itude

×10−18

−2

02

Pulse

ampl

itude



Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Figure 8: Transmitted ADS-B radar signal and received signals from four sensors.

target locations. However, for the pilots to react on imminentcollisions, it is critical to improve the convergence rate andestimation accuracy in spite of the low measurement updaterate. Overall, the convergence rate of SPF is about 2-3 timesfaster than PF, and the estimation error of SPF is about one-half of the estimation error using standard PF. The gain issignificant especially during the very first fewmeasurements.

5. Simulation Results

In order to ascertain the effectiveness of the algorithm forthe ADS-B radar system, the transmitted signal is simulatedaccording to the ADS-B radar message format, which followsthe requirements of theADS-B signal specification.TheADS-B radar waveform, as shown in Figure 7, is generated byadding random biphasemodulation into each bit of the ADS-B signal. The signals received by multiple sensors are essen-tially the attenuated and delayed replica of the transmittedsignal with measurement noise, 𝜎

𝑛. The sensor locations

are not restricted, and in the simulation setup, the sensorsare circularly positioned and 30 meters apart, using themaximum available distance on an airplane. The transmitted

ADS-B radar signal and the returned signals received bythe four sensors are shown in Figure 8. The time differencesbetween the transmitted and received signals are used tocalculate the ranges from the target to each of the sensors.Since theADS-B radar signal is broadcast every second, a newmeasurement will also arrive approximately once per second.

Without prior estimation of motion model, additionalpositional uncertainty is incorporated in the state transitionequation, as described in Section 4.2.1, to allow the particlesto have the potential to move around and to compensate theunknown maneuver while searching for the best solution.As long as one or a few particles are able to “follow” thetarget, the calculated weights will be high, and subsequently,in the next resampling step, a large amount of particles will bedrawn to the neighboring regions of the particles that havehigh weights. In the simulation, the positional uncertaintyis set to 300m after considering the relative aircraft speedand the finite amount of particles. By taking into accountthe detection range of 10 km and the computational load formultitarget tracking, the number of particles is chosen to be10,000 and𝑁threshold = 5% for the following three simulationscenarios: (1) constant velocity with random acceleration and


−6500 −6000 −5500 −5000 −4500 −4000 −35005500

6000

6500

7000

7500

8000

8500

𝑥 (m)

𝑦(m

)

True and estimated trajectories

True targetPFSPF

Figure 9: Tracking trajectories of PF and SPF methods against truetarget (20MC trials).

0 2 4 6 8 10 12 14 16 18 200

500

1000

1500

2000

2500

Number of measurements

Rang

e err

or (m

)

Range errors for one trial

PFSPF

Figure 10: Range errors during each iteration (one trial).

direction noise, (2) basic flight maneuvers, and (3) multipletargets.

To test the tracking capability, we first assume a non-cooperative aircraft having an RCS of 20 dBsm at a range of10 km. According to the link budget analysis, as discussedin Section 3.2, the SNR is 11.1 dB. The target is moving ata constant velocity of 150m/s with a Gaussian distributedacceleration and heading direction. Figure 9 depicts thetracking performance comparison of PF and SPF against the

PFSPF

0

200

400

600

800

1000

1200

RMSE

(m)

RMSE for 20 MC trials

0 2 4 6 8 10 12 14 16 18 20Number of measurements

Figure 11: RMSE for a target with constant velocity, as well asGaussian distributed acceleration and heading direction (20MCtrials).

−4000 −3000 −2000 −1000 0 1000 2000 30002500

3000

3500

4000

4500

5000

𝑥 (m)

𝑦(m

)

True and estimated trajectories

True targetPFSPF

Figure 12: Tracking performance for a maneuvering target (20MCtrials).

true target. In addition, the range errors of one trial andthe root-mean-square error (RMSE) of twenty Monte Carlo(MC) trials are plotted in Figures 10 and 11.The plots indicatethat SPF estimated location converged to the true targetlocation much faster, especially during the first few seconds.The range RMSEs of SPF and PF estimates at the 5th, 10th,and 15th seconds are listed in Table 3. Note that the differencebetween the RMSE values of the two tracking methods


PFSPF

0 5 10 15 20 25 30 35 40 450

500

1000

1500

2000

2500

Number of measurements

Rang

e err

or (m

)

Range errors for one trial

Figure 13: Range errors during each iteration (one trial).

PFSPF

0 5 10 15 20 25 30 35 40 45Number of measurements

0

200

400

600

800

1000

1200

1400

1600

1800

2000

RMSE

(m)

RMSE for 20 MC trials

Figure 14: RMSE for a maneuvering target (20MC trials).

increases during the first few measurements, because eachnew SPF estimate intelligently exploits prior estimates closerto the true target location which is not done when using PF.After convergence, SPF also performs better than PF in termsof RMSE.

In the following simulation, the benefit of the addi-tive positional uncertainty will be shown through trackinga maneuvering target even though the motion model isunknown. For a noncooperative and maneuvering target5 km away, Figure 12 shows that both PF and SPF are capableto track the target with decent accuracy. Again, 20MC trialsare performed, and the range error for each iteration and the

PFSPF

𝑥 (m)

𝑦(m

)

−4000

−4000

−3000

−3000

−2000 −1000 0 1000

−5000

−5000−5500

−4500

−3500

−2500Tracking two targets

True target 1True target 2

Figure 15: Tracking performance formultiple targets (20MC trials).

Table 3: RMSE comparison (20 MC trials).

RMSE after5 seconds

RMSE after10 seconds

RMSE after15 seconds

PF 885.2m 488.7m 289.6mSPF 212.9m 146.5m 45.33m

RMSE results are depicted in Figures 13 and 14, respectively.Compared to the target at 10 km away in the first example, theerror in the position estimate rolls off much faster when thetarget is closer to the sensors. Nevertheless, the improvementof SPF over PF is still noticeable in terms of convergence rateand estimation accuracy.

A multitarget scenario is simulated as well to test thecapability to track multiple surrounding targets simultane-ously. Since this paper focuses on the noncooperative targets,we assume two noncooperative targets located relative toown aircraft at −4000m, −3000m and −5000m, −5000m,respectively. The relative speeds are 100m/s and 150m/s, andthe trajectories are drawn in Figure 15. Even though the twotarget trajectories cross over, the proposed method is stillable to provide satisfactory estimated trajectories. However,similar to PSR, the target identification is not included inthe radar report and trajectory, for each aircraft needs to beconstructed using data associationmethods, which is anotherdiscrete problem.

6. Conclusions

TheADS-B system is considered as a cooperative surveillancetechnique to improve situational awareness while the ADS-B radar system is an innovative add-on implementation thatexploits the standard ADS-B system with only a few addi-tional integrated devices for the detection of noncooperativetargets. It is significant to point out the independence of


the communication and radar capability, because the radarperformance will not be deteriorated in the event of anincorrect ADS-B message. The communication message isencoded using PPM while the radar signature is embeddedin the sequence of the random phase modulation.

The ADS-B radar signal and system design are discussedthoroughly in this paper, and computer simulation demon-strates that the proposed approach allows the ADS-B radarsystem to track multiple noncooperative targets simultane-ously. This research work opens a lot of opportunities forthe ADS-B system to incorporate airborne sense-and-avoidcapability, which can be useful for the purpose of collisionavoidance for resource-limited UAVs [38, 39]. Moreover,the radar functionality of the ADS-B radar system has thepotential to serve as a backup surveillance in the event of lossofGNSS function andmake the communication system spoofresistant to incorrect ADS-B reports.

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Research Article Tracking of Noncooperative Airborne Targets …downloads.hindawi.com/journals/ijae/2013/521630.pdf · 2019. 7. 31. · InternationalJournalof Aerospace Engineering