基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

吴卫华; 江晶; 冯讯; 刘重阳

doi:10.11999/JEIT141232

基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

doi: 10.11999/JEIT141232

基金项目:

国家自然科学基金(61102168)资助课题

计量
- 文章访问数: 1561
- HTML全文浏览量: 133
- PDF下载量: 418
- 被引次数: 8
出版历程
- 收稿日期: 2014-09-23
- 修回日期: 2014-12-15
- 刊出日期: 2015-06-19

Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

摘要

摘要: 为在新兴的随机有限集(RFS)框架下充分利用多普勒信息跟踪杂波环境下的多目标，该文提出基于高斯混合势化概率假设密度(GM-CPHD)的脉冲多普勒雷达多目标跟踪(MTT)算法。该算法在标准GM-CPHD基础上，在使用位置量测更新状态后，再利用多普勒量测进行序贯更新，可获得更精确的似然函数和状态估计。仿真结果验证了该算法的有效性，表明在GM-CPHD基础上引入目标的多普勒信息可有效抑制杂波，显著改善跟踪性能。
- 多目标跟踪 /
- 随机有限集 /
- 概率假设密度 /
- 高斯混合势化概率假设密度 /
- 脉冲多普勒雷达
Abstract: In order to take full advantage of Doppler information for Multi-Target Tracking (MTT) in the clutter environment under the framework of emerging Random Finite Sets (RFS), an MTT algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) for pulse Doppler radar is proposed. Based on the standard GM-CPHD, the target states are updated sequentially using Doppler measurements after updating them using position measurements, then more accurate likelihood function and state estimation are obtained. Simulation results show the effectiveness of the proposed algorithm, and the introduced Doppler information can effectively suppress clutter and evidently improve tracking performance.

HTML全文

参考文献(16)

Mahler R. Statistical Multisource-multitarget Information Fusion[M]. Norwood: Artech House, 2007.

Reuter S, Wilking B, Wiest J, et al.. Real-time multi-object tracking using random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2666-2678.

Zhou G, Pelletier M, Kirubarajan T, et al.. Statically fused converted position and Doppler measurement Kalman filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 300-318.

Ulmke M, Erdinc O, and Willett P. GMTI tracking via the Gaussian mixture cardinalized probability hypothesis density filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 1821-1833.

Yoon J H, Kim D Y, Bae S H, et al.. Joint initialization and tracking of multiple moving objects using Doppler information[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3447-3452.

Vo B-N and Ma W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104.

Vo B T, Vo B N, and Antonio C. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567.

Ouyang C, Ji H, and Tian Y. Improved Gaussian mixture CPHD tracker for multitarget tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 1177-1191. [9] 欧阳成, 姬红兵, 张俊根. 一种改进的CPHD多目标跟踪算法[J]. 电子与信息学报, 2010, 32(9): 2112-2118.

Ouyang C, Ji H, and Zhang J. Improved CPHD filter for multitarget tracking[J]. Journal of Electronics Information Technology, 2010, 32(9): 2112-2118.

Beard M, Vo B T, Vo B N, et al.. A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2835-2844.

Ristic B, Clark D, Vo B N, et al.. Adaptive target birth intensity for PHD and CPHD filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1656-1668.

Beard M, Vo B-T, and Vo B-N. Multitarget filtering with unknown clutter density using a bootstrap GMCPHD filter[J]. IEEE Signal Processing Letters, 2013, 20(4): 323-326.

Ristic B, Clark D, and Gordon N. Calibration of multi-target tracking algorithms using non-cooperative targets[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 390-398.

Battistelli G, Chisci L, Fantacci C, et al.. Consensus CPHD filter for distributed multitarget tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 508-520.

Uney M, Clark D, and Julier S. Distributed fusion of PHD filters via exponential mixture densities[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 521-531.

Ristic B, Vo B-N, Clark D, et al.. A metric for performance evaluation of multi-target tracking algorithms[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3452-3457.

施引文献

期刊类型引用(6)

1.	黄鹤，郭璐，许哲，王会峰，孟芸，代亮. 集群无人机定位信号的自适应GM-CBMeMBer滤波算法. 中国惯性技术学报. 2019(04): 492-498 . 百度学术
2.	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
3.	姜学军，孙敬怡. 无线传感网络数据集离群目标跟踪方法仿真. 计算机仿真. 2018(01): 265-268 . 百度学术
4.	彭华甫，黄高明，田威，邱昊. 幅度及多普勒信息辅助的多目标跟踪算法. 航空学报. 2018(10): 234-241 . 百度学术
5.	魏立兴，孙合敏，吴卫华，黄志良. 改进的GM-CBMeMBer机载多普勒雷达多目标跟踪算法. 空军预警学院学报. 2017(03): 162-166+178 . 百度学术
6.	李文娟，顾红，苏卫民. 基于多伯努利概率假设密度的扩展目标跟踪方法. 电子与信息学报. 2016(12): 3114-3121 . 本站查看