Track-before-detect using Gaussian particle probability hypothesis density

被引：0

作者：

Li, Cui-Yun ^{[1
]}

Cao, Xiao-Nan ^{[1
]}

Liao, Liang-Xiong ^{[1
]}

Jiang, Zhou ^{[1
,2
]}

机构：

[1] School of Electronic Engineering, Xidian University, Xi'an

[2] Unit 95972 of the PLA, Jiuquan

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2015年 / 37卷 / 04期

关键词：

Gaussian particle filter (GPF); Infrared image; Multiple-target tracking; Probability hypothesis density (PHD); Track-before-detect (TBD);

D O I：

10.3969/j.issn.1001-506X.2015.04.03

中图分类号：

学科分类号：

摘要：

In order to avoid the low tracking accuracy and high complexity problems in the conventional algorithms, a novel track-before-detect algorithm based on probability hypothesis density (PHD) filter is proposed for the tracking and detection of the multiple dim targets in the infrared image. With the Gaussian particle filter, the Gaussian components in PHD can be operated recursively and extracted as the states of targets. The algorithm can realize the tracking and detection of the multiple dim targets by the energy accumulation. With the theory of the random finite set, the algorithm performs the multiple dim targets tracking with unknown number. It can not only make use of the nonlinear estimation ability of the particle filter but also avoid the tracking inaccuracy which is brought by the fuzzy clustering. Simulation results with the infrared images show that the proposed algorithm has the low complexity and the better performance in the detection and tracking multiple dim targets than the conventional algorithm. ©, 2015, Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics. All right reserved.

引用

页码：740 / 745

页数：5

共 19 条

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