Maneuvering target track-before-detect via multiple-model Bernoulli particle filter

Abstract

Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target’s maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo (particle) technique. Target detection was accomplished through the estimation of target’s existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

Keywords

Bernoulli filter / multiple model / target maneuver / track-before-detect (TBD) / sequential Monte Carlo (SMC) technique

References

[1] TonissenS M, Bar-ShalomY. Maximum likelihood trackbefore-detect with fluctuating target amplitude [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3): 796-809

[2] MoyerL R, JeffreyS, PeterL. A multi-dimensional Hough transform-based track-before-detect technique for detecting weak targets in strong clutter backgrounds [J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(4): 3062-3068

[3] GrossiE, LopsM, VenturinoL. A novel dynamic programming algorithm for track-before-detect in radar systems [J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2608-2619

[4] JohnstonL A, KrishnamuthyV. Performance analysis of a dynamic programming track-before-detect algorithm [J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(1): 228-242

[5] MorelandeM R, KreucherC M, KastellaK. A Bayesian approach to multiple target detection and tracking [J]. IEEE Transactions on Signal Processing, 2007, 55(5): 1589-1604

[6] ZhanR-h, WanJ-W. Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation [J]. IEEE Signal Processing Letters, 2006, 13(7): 445-448

[7] ArulampalamS, MaskellS, GordanN, ClappT. A tutorial on particle filter for on-line nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188

[8] ZhanR-h, XinQ, WanJ-w. Modified unscented particle filter for nonlinear Bayesian tracking [J]. Journal of Systems Engineering and Electronics, 2008, 19(1): 7-14

[9] DaveyS J, RuttenM G, CheungB. Using phase to improve track-before-detect [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 832-849

[10] WangG-h, TanS-c, GuanC-b, WangN, LiuZ-l. Multiple model particle filter track-before-detect for range ambiguous radar [J]. Chinese Journal of Aeronautics, 2013, 26(6): 1477-1487

[11] TangX, SuJ-z, ZhaoF-b, ZhouJ, WeiP. Particle filter track-before-detect implementation on GPU [J]. EURASIP Journal on Wireless Communications and Networking, 2013, 38: 11-19

[12] ZhanR-h, GaoY-z, HuJ-m, ZhangJ. SMC-PHD based multitarget track-before-detect with nonstandard point observations model [J]. Journal of Central South University, 2015, 22(1): 232-240

[13] RisticB, VoB T, VoB N, FarinaA. A Tutorial on Bernoulli filters: Theory, implementation and applications [J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3406-3430

[14] WongS, VoB T, PapiF. Bernoulli forward-backward smoothing for track-before-detect [J]. IEEE Signal Processing Letters, 2014, 21(6): 727-731

[15] RisticB, ArulampalamS, GordonN. Beyond the Kalman filter: Particle filters for tracking applications [M]. Boston: Artech House, 2004241-245

[16] GongY-x, YangH-w, HuW-d, YuW-X. Multiple model particle filter based track-before-detect for maneuvering weak target [J]. Journal of Electronics & Information Technology, 2008, 30(4): 941-944

[17] MahlerR. Statistical Multisource-multitarget information fusion [M]. Boston: Artech House, 2007711-712

[18] YangW, FuY-w, LongJ-q, LiX. Random finite sets-based joint manoeuvring target detection and tracking filter and its implementation [J]. IET Signal Processing, 2012, 6(7): 648-660

[19] GordonN, MaskellS, KirubarajanT. Efficient particle filters for joint tracking and classification [C]. Proceedings of SPIE. Orlando, 2002439-449

[20] SchuhmacherD, VoB T, VoB N. A consistent metric for performance evaluation of multiobject filters [J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447-3457