Extended GMPHD with amplitude information for multi-sensor multi-target tracking

被引：2

作者：

Ma W. ^{[1
]}

Jing Z. ^{[1
]}

Dong P. ^{[1
]}

机构：

[1] The three authors are with School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai

来源：

Aerospace Systems | 2021年 / 4卷 / 4期

基金：

中国国家自然科学基金;

关键词：

Amplitude information; Iterated-corrector; Multi-sensor fusion; Multi-target tracking; PHD filter; Random Finite Set;

D O I：

10.1007/s42401-021-00113-x

中图分类号：

学科分类号：

摘要：

To make a better discrimination between target and false alarm, amplitude information (AI) of radar signal has been incorporated into many target tracking algorithms, such as probabilistic data association, multiple hypothesis tracking and probability density hypothesis (PHD). In this paper, we present the cubature integration based Gaussian mixture implementation of the PHD filter with AI, namely the GMPHD-AI filter. Since the utilization of multiple sensors is more effective than using only one sensor, we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion. The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario. © 2021, Shanghai Jiao Tong University.

引用

页码：271 / 279

页数：8

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