A novel fast partitioning algorithm for extended target tracking using a Gaussian mixture PHD filter

被引:70
作者
Zhang, Yongquan [1 ]
Ji, Hongbing [1 ]
机构
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Extended-target tracking (ETT); ART Partitioning; Distance Partitioning; Overestimation of target number; Extended target Gaussian mixture PHD (ET-GM-PHD) filter; HYPOTHESIS; OBJECT;
D O I
10.1016/j.sigpro.2013.04.006
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
In an extended target PHD filter, the exact filter requires all possible partitions of the current measurement set for updating, which is computationally intractable. In order to limit the number of partitions, a fast partitioning algorithm for extended target Gaussian mixture PHD (ET-GM-PHD) filter is proposed, which substitutes Distance Partitioning with a fuzzy ART model. Alternative partitions of the measurement set are generated by the different vigilance values in ART. Suitable measures and remedies are given to handle the problems arisen by overestimation of target number and spatially close targets. The simulation results show that the proposed algorithm can well handle the close-spaced targets and obviously reduce computational burden without losing tracking performance, which implies good application prospects for the real-time extended target tracking system. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:2975 / 2985
页数:11
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