New and better algorithm for multitarget tracking in dense clutter

Xining Ye, Quan Pan, Ming Chen, Xin Yu, Hongcai Zhang

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

JPDA (joint probability data association) algorithm and variations of JPDA - JIPDA and IJPDA - all appear to suffer from using a feasible rule that is not quite in accord with real environment. We propose a new algorithm, generalized probability data association algorithm (GPDA), which is based on a new feasible rule, and we hope it is better for multitarget tracking in dense clutter. The paper offers a new feasible rule, which considers that measurements and targets may be used repeatedly. The paper takes the careful definition of a generalized joint event as being composed of two dummy events. This paper discusses how to use Bayes' rule to calculate the marginal probability βit. Computer simulation results show that the algorithm we propose for multitarget tracking in dense clutter is better in three aspects: lower tracking ratio loss, higher tracking precision and less computational burden.

源语言英语
页(从-至)388-391
页数4
期刊Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
22
3
出版状态已出版 - 6月 2004

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