多模型GM-CBMeMBer滤波器及航迹形成

连峰; 韩崇昭; 李晨

doi:10.3724/SP.J.1004.2014.00336

多模型GM-CBMeMBer滤波器及航迹形成

doi: 10.3724/SP.J.1004.2014.00336

1.
西安交通大学电信学院综合自动化研究所智能网络与网络安全教育部重点实验室西安 710049;
2.
西安交通大学软件学院西安 710049

基金项目:

国家重点基础研究发展计划（973计划）（2013CB329405）;国家自然科学基金创新研究群体（61221063）;中国博士后科学基金（20100481338）;中国博士后科学基金特别资助项目（2012T50746）;中央高校基本科研业务费专项资金资助

详细信息

作者简介:
连峰西安交通大学综合自动化研究所副教授. 主要研究方向为目标跟踪.E-mail：lianfeng1981@gmail.com

计量
- 文章访问数: 1971
- HTML全文浏览量: 100
- PDF下载量: 861
- 被引次数: 0
出版历程
- 收稿日期: 2012-07-20
- 修回日期: 2013-01-28
- 刊出日期: 2014-02-20

Multiple-model GM-CBMeMBer Filter and Track Continuity

1.
Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS Lab), Institute of Integrated Automation, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049;
2.
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049

Funds:

Supported by National Basic Research Program of China (973 Program) (2013CB329405), Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61221063), China Postdoctoral Science Foundation (20100481338), China Postdoctoral Science Foundation-Special Fund (2012T50746), and Fundamental Research Funds for the Central University

摘要

摘要: 提出了一种可适用于杂波环境下对多个机动目标进行跟踪并能形成多目标航迹的多模型势平衡多目标多伯努利（Cardinality balanced multi-target multi-Bernoulli，CBMeMBer）滤波器. 随后，在多机动目标时间演化模型和观测模型均为线性高斯的假设条件下利用高斯混合（Gaussian mixture，GM）技术获得了该滤波器解析的递推形式——-多模型 GM-CBMeMBer 滤波器，并简要给出了它在非线性条件下的扩展卡尔曼（Extended Kalman，EK）滤波近似. 仿真实验结果表明所建议的多模型 GM-CBMeMBer 滤波器能有效地对多个机动目标进行跟踪而单模型 GM-CBMeMBer 滤波器则会产生明显的航迹丢失和虚假航迹，并且对于信噪比较低的仿真场景，它的性能优于多模型高斯混合概率假设密度（GM probability hypothesis density，GM-PHD）滤波器，接近于多模型高斯混合势概率假设密度（GM cardinalized PHD，GM-CPHD）滤波器.
- 多机动目标跟踪 /
- 势平衡多目标多伯努利滤波器 /
- 交互式多模型算法 /
- 高斯混合实现
Abstract: A multi-model cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper for tracking multiple maneuvering targets and forming the multi-target trajectories in clutter. Given the assumptions that the dynamic and observation models of the multi maneuvering targets are linear-Gaussian and by applying the Gaussian mixture (GM) technique, the analytic recursion for the proposed filter, namely the multi-model GM-CBMeMBer filter, is obtained. The extended Kalman (EK) filtering approximations for the multi-model GM-CBMeMBer filter to accommodate non-linear models are described briefly. Simulation results show that the proposed filter performs multiple maneuvering targets tracking well whereas the single-model GM-CBMeMBer filter obviously produces the missing and false trajectories. In addition, simulation results also show that for the scenarios of the relatively low signal-to-noise ratio (SNR), the performance of the proposed filter is better than that of the multi-model GM probability hypothesis density (GM-PHD) filter, and is close to that of the multi-model GM cardinalized PHD (GM-CPHD) filter.

HTML全文

参考文献(16)

[1]	Goodman I R, Mahler R P, Nguyen H T. Mathematics of Data Fusion. Norwood, MA: Kluwer Academic, 1997. 157-218
[2]	Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178
[3]	Mahler R P S. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543
[4]	Mahler R P S. Statistical Multisource-Multitarget Information Fusion. Norwood, MA: Artech House, 2007. 235-278
[5]	Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245
[6]	Clark D E, Bell J M. Multi-target state estimation and track continuity for the particle PHD filter. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1441-1453
[7]	Liu W F, Han C Z, Lian F, Zhu H Y. Multitarget state extraction for the PHD filter using MCMC approach. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2): 864-883
[8]	Vo B T, Vo B N, Cantoni A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Transactions on Signal Processing, 2009, 57(2): 409-423
[9]	Reza H, Vo B N, Vo B T, David S. Bayesian integration of audio and visual information for multi-target tracking using a CB-member filter. In: Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011. Prague, Czech Republic: IEEE, 2011. 2300-2303
[10]	Ouyang Cheng, Ji Hong-Bing, Gou Zhi-Qiang. Improved multiple model particle PHD and CPHD filters. Acta Automatica Sinica, 2012, 38(3): 341-348 (欧阳成, 姬红兵, 郭志强. 改进的多模型粒子PHD和CPHD滤波算法. 自动化学报, 2012, 38(3): 341-348)
[11]	Xiong Bo, Gan Lu. Multiple maneuvering targets tracking using MM-CBMeMBer filter. Journal of Radars, 2012, 1(3): 238-245 (熊波, 甘露. MM-CBMeMBer滤波器跟踪多机动目标. 雷达学报, 2012, 1(3): 238-245)
[12]	Li Hui-Ping, Xu De-Min, Zhang Fu-Bin, Yao Rao. Consistency analysis of EKF-based SLAM by measurement noise and observation times. Acta Automatica Sinica, 2009, 35(9): 1177-1184 (李慧平, 徐德民, 张福斌, 姚尧. 基于量测噪声和观测次数的EKF-SLAM一致性分析. 自动化学报, 2009, 35(9): 1177-1184)
[13]	Pasha S A, Vo B N, Tuan H D, Ma W K. A Gaussian mixture PHD filter for jump Markov system models. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919-936
[14]	Georgescu R A, Willett P K. The multiple model CPHD tracker. IEEE Transactions on Signal Processing, 2012, 60(4): 1741-1751
[15]	Panta K R, Vo B N, Clark D E. An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. In: Proceedings of the 4th International Conference on Intelligent Sensing and Information. Bangalore: IEEE, 2006. 230-235
[16]	Zhang Heng, Fan Xiao-Ping, Qu Zhi-Hua. Mobile robot adaptive Monte Carlo localization based on multiple hypothesis tracking. Acta Automatica Sinica, 2007, 33(9): 941-946 (张恒, 樊晓平, 瞿志华. 基于多假设跟踪的移动机器人自适应蒙特卡罗定位研究. 自动化学报, 2007, 33(9): 941-946)