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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11752

Title: Applying Likelihood on Hopfield Neural Network for Radar Tracking
Authors: Chung, Yi-Nung;Yang, Maw-Rong;Juang, Dend-Jyi;Hsu, Tsung-Chun;Hsu, Shun-Peng
Contributors: 電機工程學系
Keywords: Data association;Likelihood;Neural network
Date: 2008-03
Issue Date: 2012-07-02T02:07:12Z
Publisher: Chinese Institute of Engineers
Abstract: The multiple‐target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.
Relation: Journal of The Chinese Institute of Engineers, 31(2): 339-342
Appears in Collections:[電機工程學系] 期刊論文

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