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.
作者
陈树新
洪磊
吴昊
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)
出处
《电子与信息学报》
EI
CSCD
北大核心
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)~~
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
Multi-Target Tracking(MTT)
Heavy-tailed noise
Cardinality Balanced Multi-target Multi-Bernoulli(CBMeMBer)
Student’s t distribution
Closed recursive framework
