学生t混合势均衡多目标多伯努利滤波器  

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

作　　者：陈树新[1] 洪磊 吴昊[1] 刘卓崴 岳龙华 CHEN Shuxin;HONG Lei;WU Hao;LIU Zhuowei;YUE Longhua(Institute of Information and Navigation,Aire Force Engineering University,Xi’an 710077,China;Air Force Research Institute,Beijing 100096,China;Unit 93658,Beijing 100144,China)

机构地区：[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

出　　处：《电子与信息学报》2019年第10期2457-2463,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61703420,61673392)~~

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。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.

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 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]