学生 <i>t</i> 混合势均衡多目标多伯努利滤波器

陈树新; 洪磊; 吴昊; 刘卓崴; 岳龙华

doi:10.11999/JEIT181121

学生 t 混合势均衡多目标多伯努利滤波器

doi: 10.11999/JEIT181121

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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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

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。
- 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.
- 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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图 1 目标数估计

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图 2 OSPA距离

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图 3 不同污染率下的平均OSPA距离

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图 4 不同杂波率下的OSPA距离

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