Gaussian mixture reduction based on fuzzy ART for extended target tracking

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• 作者：Yongquan Zhang ; Hongbing Ji
• 关键词：Global clustering algorithm ; Gaussian mixture reduction ; Weighted Kullback&ndash ; Leibler difference ; Normalized integrated squared distance measure ; Extended target tracking
• 刊名：Signal Processing
• 出版年：April, 2014
• 年：2014
• 卷：97
• 期：Complete
• 页码：232-241
• 全文大小：722 K

文摘

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 KL 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.