Iterative-Mapping PHD Filter for Extended Targets Tracking

被引：0
|
作者：
Dai, Biao [1 ]
Li, Cuiyun [1 ]
Ji, Hongbing [1 ]
Hu, Yuhen [2 ]
机构：
[1] Xidian Univ, Dept Elect Engn, Xian, Peoples R China
[2] Univ Wisconsin, Dept Elect & Comp Engn, Madison, WI 53706 USA
来源：
FOURTH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (CCAIS 2015) | 2015年
关键词：
iterative-mapping; probability hypothesis density; multiple extended targets tracking; sequential monte carlo; RANDOM FINITE SETS; MULTITARGET TRACKING;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel Iterative-Mapping algorithm is proposed to alleviate the computation intractable measurement partition problem in a Probability Hypothesis Density tracking filter for extended targets. The iterative mapping procedure is based on the assumption that the target centroids can be estimated as the conditional expectation of measurements. Using sequential Monte Carlo method, this algorithm iteratively maps the target states and measurement sources each other. It can be applied to track multiple extended targets. Simulation results demonstrate superior performance of this proposed method over traditional extended target PHD filter with partition.
引用
收藏
页码：85 / 88
页数：4
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