Multi-Target Tracking Based on Multi-Bernoulli Filter with Amplitude for Unknown Clutter Rate

Sensors

Changshun Yuan²  Jun Wang¹  Peng Lei²  Yanxian Bi²  Zhongsheng Sun² 

[1] School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

关键词: multi-Bernoulli filter;    random finite set;    multi-target tracking;    amplitude information;    clutter rate estimation;    sequential Monte-Carlo;   

DOI  :  10.3390/s151229804

来源: mdpi

【 摘 要 】

Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice. In this paper, an improved multi-Bernoulli filter based on random finite sets for multi-target Bayesian tracking accommodating non-linear dynamic and measurement models, as well as unknown clutter rate, is proposed for radar sensors. The proposed filter incorporates the amplitude information into the state and measurement spaces to improve discrimination between actual targets and clutters, while adaptively generating the new-born object random finite sets using the measurements to eliminate reliance on prior random finite sets. A sequential Monte-Carlo implementation of the proposed filter is presented, and simulations are used to demonstrate the proposed filter’s improvements in estimation accuracy of the target number and corresponding multi-target states, as well as the clutter rate.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.