Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking

YANG Feng; ZHANG Wanying

doi:10.11999/JEIT160467

Volume 39 Issue 3

Mar. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(3): 634-639

YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

Citation:

YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

Citation:

PDF( 283 KB)

Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking

doi: 10.11999/JEIT160467

YANG Feng¹,
ZHANG Wanying¹

Funds:

The National Natural Science Foundation of China (61135001, 61374159, 61374023), Seed Foundation of Innovation and Creation of Graduate Students in Northwestern Polytechnical University (Z2016149)

Received Date: 2016-05-09
Rev Recd Date: 2016-11-28
Publish Date: 2017-03-19

Abstract

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.
- Maneuvering target tracking,
- Bernoulli filter,
- Particle filter,
- Multiple model

FullText(HTML)

References(16)

References

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(16)

1.	李冀，周战洪，贺红林，刘文光，李怡庆. 基于围猎改进哈里斯鹰优化的粒子滤波方法. 电子与信息学报. 2023(06): 2284-2292 . 本站查看
2.	党晓方，蔡兴雨. 基于Transformer的机动目标跟踪技术. 电子科技. 2023(09): 86-92 .
3.	李君龙，周荻，王冠，陈晓波，秦雷. 临近空间目标跟踪与预报技术研究. 现代防御技术. 2021(03): 1-12+29 .
4.	杨标，朱圣棋，余昆，房云飞. 贪婪的量测划分机制下的多传感器多机动目标跟踪算法. 电子与信息学报. 2021(07): 1962-1969 . 本站查看
5.	薛秋条，宁巧娇，吴孙勇，蔡如华，伍雯雯. 基于JMS-SMC-PHD滤波的检测前跟踪算法. 红外技术. 2020(08): 783-788 .
6.	逯志宇，王建辉，秦天柱，巴斌. 基于对称旋转不变性的非圆相干分布源直接定位算法. 电子与信息学报. 2019(03): 537-543 . 本站查看
7.	王洪雁，邱贺磊，郑佳，裴炳南. 光照变化下基于逆向稀疏表示的视觉跟踪方法. 电子与信息学报. 2019(03): 632-639 . 本站查看
8.	昝孟恩，周航，韩丹，杨刚，许国梁. 粒子滤波目标跟踪算法综述. 计算机工程与应用. 2019(05): 8-17+59 .
9.	卓奕弘，姜秋喜，刘鑫，刘少平，张武兵. 解决雷达方位角突变问题的一种方法. 现代雷达. 2019(03): 53-57 .
10.	吕晓华，张群英，刘新，陈忠诚，方广有. 一种便携式地面监视雷达人体目标跟踪算法. 电子测量技术. 2019(13): 6-10 .
11.	张博伦，周荻，吴世凯. 临近空间高超声速飞行器机动模型及弹道预测. 系统工程与电子技术. 2019(09): 2072-2079 .
12.	刘大千，刘万军，费博雯. 局部感知下的稀疏优化目标跟踪方法. 电子与信息学报. 2018(02): 272-281 . 本站查看
13.	杨丹，姬红兵，张永权. 未知杂波条件下样本集校正的势估计概率假设密度滤波算法. 电子与信息学报. 2018(04): 912-919 . 本站查看
14.	冯翔，赵占锋，赵宜楠，周志权. 基于矩阵加权多模型融合的认知跟踪波形设计. 哈尔滨工业大学学报. 2018(05): 30-37 .
15.	樊翠玲. 改进粒子滤波的锂电池SOC估算. 实验室研究与探索. 2018(01): 134-138 .
16.	逯志宇，巴斌，任衍青，王大鸣. 基于进化粒子滤波的数据域直接跟踪方法. 系统工程与电子技术. 2018(05): 968-975 .