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

• Main Authors: Zhuowei Liu, Shuxin Chen, Hao Wu, Renke He, Lin Hao
• Format: Article
• Language: English
• Published: MDPI AG 2018-04-01
• Series: Sensors
• Subjects: multi-target tracking; PHD filter; Student’s t mixture; outliers; robustness
• Online Access: http://www.mdpi.com/1424-8220/18/4/1095

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.