Probability Hypothesis Densities for Multi-sensor,Multi-target Tracking with Application to Acoustic Sensors Array
Random sets theory offers a uniform framework for the multi-source data fusion, and all problems of the data fusion could be describe, analyzed and solved in this framework. The multi-sensor multi-target tracking problem could be natural represented in the framework. It is of engineering importance to tracking low altitude moving targets with acoustic methods due to the blindness of the traditional radar detecting. In this paper, an algorithm for tracking the low altitude or ground moving targets is put forward based on the Probability Hypothesis Density (PHD) Filter. The PHD Filter based on Finite Set Statistics doesnt need consider data association for multi-target tracking, which propagates the PHD or first moment instead of the full multi-target posterior, and it could estimating the unknown and time-varying number of targets and their states under clutter environment. In the practical, we use the Sequential Monte Carlo (SMC) method to approximate the PHD. The paper presents a novel and fundamentally well-grounded framework for tracking multiple acoustic targets using PHD Filter and passive acoustic localization technique. Simulations are also presented to demonstrate the performance in tracking a randomly varying number of targets in a clutter environment.
Data fusion Multi-target tracking PHD Filter Random set Passive acoustic localization Monte Carlo method
Lin Xiaodong Zhu Linhu Li Zhengxin
Engineering Institute Air Force Engineering University,AFEU Xi an China Engineering Institute Air Force Engineering University,AFEU Xian China
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
218-222
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）