基于多模粒子滤波的机动弱目标检测前跟踪

龚亚信; 杨宏文; 胡卫东; 郁文贤

doi:10.3724/SP.J.1146.2006.01528

基于多模粒子滤波的机动弱目标检测前跟踪

doi: 10.3724/SP.J.1146.2006.01528

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出版历程
- 收稿日期: 2006-10-10
- 修回日期: 2007-04-02
- 刊出日期: 2008-04-19

Multiple Model Particle Filter Based Track-before-Detect for Maneuvering Weak Target

摘要

摘要: 检测前跟踪技术是低信噪比环境下目标检测与跟踪的有效方法。该文针对目标作复杂运动的情况，提出了机动弱目标检测前跟踪的多模粒子滤波算法。该算法在目标状态矢量中增加了表示目标存在与否以及目标运动模型的变量，采用粒子滤波实现了包含两个离散变量的混合滤波过程。仿真试验表明，该算法在经典跟踪方法难以发挥作用的低信噪比条件下，能够有效实现机动目标的检测与跟踪。
- 检测前跟踪; 多模; 粒子滤波; 弱目标
Abstract: Track-Before-Detect(TBD) is an efficient approach which detects and tracks targets in low SNR environment. A multiple model particle filter algorithm is presented in this paper for the maneuvering weak target which dynamics is complicated. In this algorithm a variable which denotes whether the target is existent or not and a variable which denotes the dynamics model of the target are augmented. The hybrid filter which includes two discrete variables is implemented by particle filter. Simulation results show that this algorithm can efficiently detect and track maneuvering targets on the condition of low SNR which the classical tracking methods can not work normally.

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Blackman S and Popoli R. Design and Analysis of Modern[2]Tracking Systems[M]. New York: Artech House Publishers,[3]99, ch.17: Detection and tracking of dim targets in clutter.[4]Ristic R, Arulampalam S, and Gordon N. Beyond the[5]Kalman Filter-Particle Filters for Tracking Applications[M].[6]Boston-London: Artech House 2004, ch.11:Detection and[7]tracking of stealthy targets.[8]Carlson B D, Evans E D, and Wilson S L. Search radar[9]detection and track with the Hough transform, part I: System[10]concept[J]. IEEE Trans. on Aerospace and Electronic System,[11]94, 30(1): 102-108.[12]Barniv Y. Dynamic programming algorithm for detecting[13]dim moving targets[M]. In Multitarget Multisensor Tracking:[14]Advanced Applications (Y. Bar-Shalom, ed), Norwood, MA:[15]Artech House, 1990, ch.4.[16]Arnold J, Shaw S, and Pasternack H. Efficient target tracking[17]using dynamic programming[J]. IEEE Trans. on Aerospace[18]and Electronic System, 1993, 29(1): 44-56.[19]Stone L D, Barlow C A, and Corwin T L. Bayesian Multiple[20]Target Tracking[M]. Norwood, MA: Artech House, 1999, ch.6:[21]Likelihood ratio detection and tracking: theoretical foundations.[22]Salmond D J and Birch H. A particle filter for track-beforedetect[[23]Proceedings of the American Control Conference,[24]Arlington, VA June 25-27, 2001: 3755-3760.[25]Rutten M G, Gordon N J, and Maskell S. particle-based[26]Track-Before-Detect in Rayleigh noise[J]. SPIE, 2004, Vol.[27]28: 509-519.[28]Boers Y and Driessen H. Particle Filter Based Track Before[29]Detect Algorithms[J].[J]. SPIE.2003,Vol. 5204:20-[30]Boers Y and Driessen H. Multitarget particle filter track[31]before detect application[J]. IEE Proc.-Radar Sonar Navig.,[32]04, 151(6): 351-357.[33]Torstensson J and Trieb M. Particle filtering for track before[34]detect applications[D]. [Masters Thesis], Division of Automatic[35]Control Department of Electrical Engineering, Linkping[36]University, Sweden, 2005.

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