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| Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking |
| YANG Feng ZHANG Wanying |
(School of Automation, Northwestern Polytechnical University, Xi’an 710129, China)
(Key Laboratory of Information Fusion Technology, Ministry of Education, Xi’an 710129, China) |
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Abstract Interacting Multiple Model Bernoulli Particle Filter (IMMBPF) is suitable for maneuvering target tracking under cluttered environment. However, when model information is introduced into particle sampling process in IMMBPF, it will lead to the number decline of particles which are applied to approaching the real state and model, and the computation load is heavy because of the interacting stage of particles in the recursion. An enhanced Multiple Model Bernoulli Particle Filter (MMBPF) is proposed to improve the effectiveness of single particle to approximate the real target state and model. The number of particles of each model is given in advance, and the posterior probability of each model is updated with the associate likelihood function, which avoids particle degeneracy without distorting the Markov property. Simulation results show that the proposed MMBPF achieves better tracking performance with fewer particles than IMMBPF.
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Received: 09 May 2016
Published: 11 January 2017
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| Fund: The National Natural Science Foundation of China (61135001, 61374159, 61374023), Seed Foundation of Innovation and Creation of Graduate Students in Northwestern Polytechnical University (Z2016149) |
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Corresponding Authors:
ZHANG Wanying
E-mail: zhangwanying0418@163.com
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| [1] |
RISTIC B, VO B T, VO B N, et al. A tutorial on Bernoulli filters: Theory, implementation and applications[J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3406-3430. doi: 10.1109/TSP.2013.2257765.
|
| [2] |
VO B T, VO B N, HOSEINNEZHAD, et al. Robust multi-Bernoulli filtering [J]. IEEE Selected Topics in Signal Processing, 2013, 7(3): 399-409. doi: 10.1109/JSTSP.2013. 2252325.
|
| [3] |
PAPI F, KYOVTOROV V, GIULIANNO R, et al. Bernoulli filter for track-before-detect using MIMO radar[J]. IEEE Signal Processing Letters, 2014, 21(9): 1145-1149. doi: 10.1109/LSP.2014.2325566.
|
| [4] |
VO B T, SEE C M, MA N, et al. Multi-sensor joint detection and tracking with the Bernoulli filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1385-1402. doi: 10.1109/TAES.2012.6178069.
|
| [5] |
GRAMSTROM K, WILLETT P, and BARSHALOM Y. A Bernoulli filter approach to detection and estimation of hidden Markov models using cluttered observation sequences[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, 2015: 3911-3915. doi: 10.1109/ICASSP.2015.7178704.
|
| [6] |
BLOM H A P. An efficient filter for abruptly changing systems[C]. IEEE Proceedings of 23th Conference on Decision and Control, Las Vegas, NV, USA, 1984, Vol.23: 656-658. doi: 10.1109/CDC.1984.272089.
|
| [7] |
MCGINNITY S and IRWIN G W. Multiple model bootstrap filter for maneuvering target tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 1006-1012. doi: 10.1109/7.869522.
|
| [8] |
刘贵喜, 高恩克, 范春宇. 改进的交互式多模型粒子滤波跟踪算法[J]. 电子与信息学报, 2007, 29(12): 2810-2813.
|
|
LIU Guixi, GAO Enke, and FAN Chunyu. Tracking algorithms based on improved interacting multiple model particle filter[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2810-2813.
|
| [9] |
BOERS Y and DRIESSEN H. Interacting multiple model particle filter[J]. IEE Proceedings-Radar, Sonar and Navigation, 2003, 150(5): 344-349. doi: 10.1049/ip-rsn: 20030741.
|
| [10] |
DRIESSEN H and BOERS Y. Efficient particle filter for jump Markov nonlinear systems[J]. IEE Proceedings-Radar, Sonar and Navigation, 2005, 152(5): 323-326. doi: 10.1049/ ip-rsn:20045075.
|
| [11] |
YANG Wei, FU Yaowen, LONG Jianqian, et al. Random finite sets-based joint maneuvering target detection and tracking filter and its implementation[J]. IET Signal Processing, 2012, 6(7): 648-660. doi: 10.1049/iet-spr. 2011.0171.
|
| [12] |
DUNNE D and KIRUBARAJAN T. Multiple model multi-Bernoulli filters for maneuvering targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2679-2692. doi: 10.1109/TAES.2013.6621845.
|
| [13] |
YANG Yanbo, ZOU Jie, YANG Feng, et al. An adaptive particle filter based on the mixing probability[C]. IEEE International Congress on Image and Signal Processing (CISP), Chongqing, China, 2012: 1480-1484. doi: 10.1109/ CSIP. 2012.6469724.
|
| [14] |
鉴福升, 徐跃民, 阴泽杰. 改进的多模型粒子滤波机动目标跟踪算法[J]. 控制理论与应用, 2010, 27(8): 1012-1016.
|
|
JIAN Fusheng, XU Yueming, and YIN Zejie. Enhanced multiple model particle filter for maneuvering target tracking[J]. Control Theory & Application, 2010, 27(8): 1012-1016.
|
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