Improved probability hypothesis density filter for multitarget tracking

被引：0
作者：
Bo Li
Fu-Wen Pang
机构：
[1] Dalian Maritime University,College of Information Science and Technology
[2] National Engineering Research Center of Maritime Navigation System,undefined
来源：
Nonlinear Dynamics | 2014年 / 76卷
关键词：
Multitarget tracking; Probability hypothesis density; Particle; False alarm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Multitarget tracking (MTT) is important in radar and/or sonar surveillance systems. However, as a popular implementation of MTT, the standard probability hypothesis density (PHD) filter has computerized intractability and imprecise estimation. To overcome these drawbacks, an improved PHD filter is proposed in this paper. First, we apply particle approximation to achieve the closed-form solution to the PHD propagations. Afterward, the weight formula is simplified by the method of kernel estimate. Meanwhile, the excess weights of newborn particles are increased to survival particles on average. As a result, the overestimated target number and the missed detection are balanced, and the accuracy of MMT is improved by our lemmas. Finally, simulations are presented to compare the performance of the proposed filter with that of the standard one. The results show that our filter can achieve MTT with better performance.
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收藏
页码：367 / 376
页数：9
相关论文
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