A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

Sensors

Hao Wu¹  Zhuowei Liu¹  Shuxin Chen¹  Renke He¹  Lin Hao² 

[1] Information and Navigation College Air Force Engineering University, Xi’an 710077, China;Unit 93786, Chinese People’s Liberation Army (PLA), Zhangjiakou 075000, China;

关键词: multi-target tracking;    PHD filter;    Student’s t mixture;    outliers;    robustness;   

DOI  :  10.3390/s18041095

来源: DOAJ

【 摘 要 】

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers.

【 授权许可】

Unknown