Gaussian mixture reduction based on fuzzy ART for extended target tracking

被引：12

作者：

Zhang, Yongquan ^{[1
]}

Ji, Hongbing ^{[1
]}

机构：

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

来源：

SIGNAL PROCESSING | 2014年 / 97卷

基金：

中国国家自然科学基金;

关键词：

Global clustering algorithm; Gaussian mixture reduction; Weighted Kullback-Leibler difference; Normalized integrated squared distance measure; Extended target tracking; ADAPTIVE PATTERN-CLASSIFICATION; PHD FILTER;

D O I：

10.1016/j.sigpro.2013.11.004

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a global Gaussian mixture (GM) reduction algorithm via clustering for extended target tracking in clutter. The proposed global clustering algorithm is obtained by combining a fuzzy Adaptive Resonance Theory (ART) neural network architecture with the weighted Kullback-Leibler (KL) difference which describes discrimination of one component from another. Therefore, we call the proposed algorithm as ART-KL clustering (ART-KL-C) in the paper. The weighted KL difference is used as a category choice function of ART-KL-C, derived by considering both the la divergence between two components and their weights. The performance of ART-KL-C is evaluated by the normalized integrated squared distance (NISD) measure, which describes the deviation between the original and reduced GM. The proposed algorithm is tested on both one-dimensional and four-dimensional simulation examples, and the results show that the proposed algorithm can more accurately approximate the original mixture and is useful in extended target tracking. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：232 / 241

页数：10

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