Random finite sets and sequential Monte Carlo methods in multi-target tracking

被引：24
|
作者：
Vo, BN [1 ]
Singh, S [1 ]
Doucet, A [1 ]
机构：
[1] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia
来源：
2003 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON RADAR | 2003年
关键词：
D O I：
10.1109/RADAR.2003.1278790
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Random finite set provides a rigorous foundation for optimat Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In this paper, we highlight the relationship between Radon-Nikodym derivative and set derivative of random finite sets that enables a Sequential Monte Carlo (SMC) implementation of the optimal multitarget filter. In addition, a generalised SMC method to implement the PHD filter is also presented. The SMC PHD filter has an attractive feature-its computational complexity is independent of the (time-varying) number of targets.
引用
收藏
页码：486 / 491
页数：6
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