陈树新¹,
洪磊¹,
吴昊^1,,,
刘卓崴²,
岳龙华³
1.空军工程大学信息与导航学院 西安 710077
2.空军研究院 北京 100096
3.93658部队 北京 100144
基金项目:国家自然科学基金(61703420, 61673392)

详细信息

作者简介:陈树新：男，1965年生，教授，博士生导师，主要研究方向为无源定位跟踪
洪磊：男，1995年生，硕士，研究方向为多目标跟踪
吴昊：男，1988年生，讲师，博士，研究方向为无源定位跟踪
刘卓崴：男，1989年生，工程师，研究方向为多目标跟踪
岳龙华：男，1975年生，工程师，研究方向为光端通信

通讯作者:吴昊　wuhaostudy@163.com

中图分类号:TP39

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被引次数:0

出版历程

收稿日期:2018-12-04
修回日期:2019-04-28
网络出版日期:2019-05-21
刊出日期:2019-10-01

Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter

Shuxin CHEN¹,
Lei HONG¹,
Hao WU^1,,,
Zhuowei LIU²,
Longhua YUE³
1. Institute of Information and Navigation, Aire Force Engineering University, Xi’an 710077, China
2. Air Force Research Institute, Beijing 100096, China
3. Unit 93658, Beijing 100144, China
Funds:The National Natural Science Foundation of China (61703420, 61673392)


摘要
摘要:在有重尾的过程噪声和量测噪声的影响下，高斯混合势均衡多目标多伯努利滤波器(GM-CBMeMBer)的滤波性能会明显下降。针对上述问题，该文提出一种新的学生 t 混合势均衡多目标多伯努利滤波器(STM-CBMeMBer)。该滤波器将过程噪声和量测噪声近似为学生 t 分布，并用学生 t 混合模型来近似多目标的先验强度。从理论上推导出学生 t 混合形式的预测强度和后验强度，建立了势均衡多目标多伯努利滤波器的闭式递推框架。仿真结果表明，在重尾的过程噪声和量测噪声存在的环境中，该滤波器能有效抑制其干扰，相比于传统方法，具有更高的跟踪精度。
关键词:多目标跟踪/
重尾噪声/
势均衡多目标多伯努利/
学生 t 分布/
闭式递推框架
Abstract:The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.
Key words:Multi-Target Tracking (MTT)/
Heavy-tailed noise/
Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer)/
Student’s t distribution/
Closed recursive framework



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