A sequential GM-based PHD filter for a linear Gaussian system  

作　　者：LIU ZongXiang 

机构地区：[1]ATR Key Laboratory, Shenzhen University

出　　处：《Science China(Information Sciences)》2013年第10期11-20,共10页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.61271107);Natural Science Foundation of Guangdong Province(Grant No.S2012010009417);Research Fund for the Doctoral Program of Higher Education of China(Grant No.20104408120001);Key Projects in the National Science & Technology Pillar Program(Grant No.2011BAH24B12)

摘　　要：The probability hypothesis density （PHD） filter provides an efficiently parallel processing method for multi-target tracking. However, measurements have to be gathered for a scan period before the PHD filter can perform a recursion, therefore, significant delay may arise if the scan period is long. To reduce the delay in the PHD filter, we propose a sequential PHD filter which updates the posterior intensity whenever a new measurement becomes available. An implementation of the sequential PHD filter for a linear Caussian system is also developed. The unique characteristic of the proposed filter is that it can retain the useful information of missed targets in the posterior intensity and sequentially handle the received measurements in time.The probability hypothesis density （PHD） filter provides an efficiently parallel processing method for multi-target tracking. However, measurements have to be gathered for a scan period before the PHD filter can perform a recursion, therefore, significant delay may arise if the scan period is long. To reduce the delay in the PHD filter, we propose a sequential PHD filter which updates the posterior intensity whenever a new measurement becomes available. An implementation of the sequential PHD filter for a linear Caussian system is also developed. The unique characteristic of the proposed filter is that it can retain the useful information of missed targets in the posterior intensity and sequentially handle the received measurements in time.

关 键 词：multi-target tracking PHD filter sequential filter Gaussian mixture linear Gaussian system 

分 类 号：TN911.7[电子电信—通信与信息系统]