Multi-sensor Gaussian Mixture PHD Fusion for Multi-target Tracking

被引：0
|
作者：
Shen-Tu H. [1 ]
Xue A.-K. [1 ]
Zhou Z.-L. [1 ]
机构：
[1] Institute of Information Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2017年 / 43卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Finite set statistics (FISST); Gaussian mixture; Multi-sensor multi-target tracking; Probability hypothesis density (PHD);
D O I：
10.16383/j.aas.2017.c170091
中图分类号：
学科分类号：
摘要：
As the performance of single sensor multi-target tracking method will degenerate under complicated environment, a multi-sensor Gaussian mixture PHD multi-target tracker is proposed in terms of FISST theory. First, the formalized PHD filter is analyzed with FISST. Then, a multi-sensor posterior PHD feedback fusion framework is constructed. Further, Gaussian mixture technique is employed to build a multi-sensor PHD tracking method. At last, three applicable algorithms are proposed by solving particle matching and fusion problem. Simulation results show that, compared to some common Gaussian mixture PHD algorithms, the proposed algorithms are more accurate and robust. Key words Copyright © 2017 Acta Automatica Sinica. All rights reserved.
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页码：1028 / 1037
页数：9
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