THE PROBABILITY HYPOTHESIS DENSITY FILTER WITH EVIDENCE FUSION  

作　　者：Liu Weifeng Xu Xiaobin 

机构地区：[1]School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

出　　处：《Journal of Electronics(China)》2009年第6期746-753,共8页电子科学学刊（英文版）

基　　金：Supports in part by the NSFC (No. 60772006, 60874105);the ZJNSF(Y1080422, R106745);NCET (08- 0345)

摘　　要：The original Probability Hypothesis Density (PHD) filter is a tractable algorithm for Multi-Target Tracking (MTT) in Random Finite Set (RFS) frameworks. In this paper,we introduce a novel Evidence PHD (E-PHD) filter which combines the Dempster-Shafer (DS) evidence theory. The proposed filter can deal with the uncertain information,thus it forms target track. We mainly discusses the E-PHD filter under the condition of linear Gaussian. Research shows that the E-PHD filter has an analytic form of Evidence Gaussian Mixture PHD (E-GMPHD). The final experiment shows that the proposed E-GMPHD filter can derive the target identity,state,and number effectively.The original Probability Hypothesis Density （PHD） filter is a tractable algorithm for Multi-Target Tracking （MTT） in Random Finite Set （RFS） frameworks. In this paper, we introduce a novel Evidence PHD （E-PHD） filter which combines the Dempster-Shafer （DS） evidence theory. The proposed filter can deal with the uncertain information, thus it forms target track. We mainly discusses the E-PHD filter under the condition of linear Gaussian. Research shows that the E-PHD filter has an analytic form of Evidence Gaussian Mixture PHD （E-GMPHD）. The final experiment shows that the proposed E-GMPHD filter can derive the target identity, state, and number effectively.

关 键 词：Probability Hypotheses Density （PHD） Dempster-Shafer （DS） evidence Uncertain in-formation Evidence PHD （E-PHD） Evidence Gaussian Mixture PHD （E-GMPHD） 

分 类 号：TN713[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]