Feature Aided Gaussian Mixture Probability Hypothesis Density Filter with Modified 2D Assignment

摘要：

In order to track multiple targets with time-varying number of targets, the paper proposed a new feature aided Gaussian mixture probability hypothesis density (FA-GM-PHD) filter, and adopts a modified 2-D assignment algorithm to carry out the data association and manage the tracks in the FA-GM-PHD filter. The target feature information incorporated into the FA-GM-PHD filter is target Doppler and target down-range extent. With two typical multi-target tracking scenarios, the simulation results in the paper have verified that the FA-GM-PHD filter has much higher correct data association probability and filtering precision of target states than GM-PHD, and it can estimate the number of targets more stably and precisely than GM-PHD. The main shortcoming of FA-GM-PHD is that it has delayed estimate of target’s number at the spawning time than GM-PHD, which will be studied in the future work.

关键词： multi-target tracking Gaussian mixture probability hypothesis density feature aided data association modified 2-D assignment

作者: Chen Ying Cheng Zhen Wen Shuliang

作者单位: Beijing institute of radio measurement of the Second Research Academy, CASIC

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 800-803

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）