Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter

被引：8

作者：

Liu, Long ^{[1
]}

Ji, Hongbing ^{[1
]}

Zhang, Wenbo ^{[1
]}

Liao, Guisheng ^{[1
]}

机构：

[1] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Multi-sensor fusion; multi-target tracking; random finite set; probability hypothesis density; MULTI-BERNOULLI FILTER; RANDOM FINITE SETS;

D O I：

10.1109/ACCESS.2019.2918256

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Compared with the single sensor tracking system, the multi-sensor tracking system has several advantages in target tracking, such as a larger field of view and higher tracking accuracy. Different from the multi-sensor filters based on the random finite set (RFS) theory, the product multi-sensor probability hypothesis density (PM-PHD) filter with a modified cardinality coefficient performs well in estimating the number of targets. Since the PM-PHD filter employs the iterative fusion structure, its state estimation is sensitive to the sensor parameters. Furthermore, to improve the cardinality estimation, the PM-PHD filter may estimate some false targets when miss-detection occurs. Addressing the above problems, this paper changes the fusion structure of the PM-PHD filter and presents a novel version of the PM-PHD filter. The main idea of the proposed algorithm is the combinations of measurement subsets and other factors. Both the cardinality estimation and the state estimation are obtained by fusing the target numbers and normalized PHDs of these combinations. Compared with other multi-sensor PHD filters, the proposed algorithm can handle the problems of miss-detection and false alarm effectively. Moreover, the simulation results and the theoretical analysis indicate that the new PM-PHD filter can deal with a harsh tracking environment.

引用

页码：67745 / 67760

页数：16

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