Maneuvering Target Tracking Based on Particle PHD Filtering

摘要：

As for the problem of maneuvering target tracking in the clutter environment,this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association,and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multimodel (TMM) algorithm in the field of model switching effectively,in the clutter environment,the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance,and the simulation results show that,the improved filtering algorithm has good tracking performance and tracking accuracy.

关键词： Target Tracking Random Sets Interactive Multiple Model Probability Density Hypothesis Sequential Monte Carlo

作者: Gao Song Gong Qian Xiao Qinkun Chen Chaobo

作者单位: School of Electronic Information Engineering,Xian Technological University,Xian,710032,China

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 430-433

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）