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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11786

Title: The Dual-Kalman Filtering and Neural Solutions to Maneuvering Estimation Problems
Authors: Chung, Yi-Nung;Juang, Dend-Jyi;Hu, Kuo-Chang;Li, Ming-Liang;Chuang, Kai-Chih
Contributors: 電機工程學系
Keywords: Maneuvering targets;Dual-Kalman filtering algorithm;Competitive hopfield neural network;Data association;Multiple-target tracking system
Date: 2010-07
Issue Date: 2012-07-02T02:08:38Z
Publisher: 中央研究院資訊科學研究所
Abstract: Tracking maneuvering targets in a radar system is more complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the maneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the maneuvering targets’ tracking problems. In this approach, two collaborative Kalman filters are devised which one for
pursuing the track estimation and the other for estimating the status of maneuver. Based on this approach, the most approximate target’s acceleration can be detected and estimated in real time. Moreover, it is also shown that one Competitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.
Relation: Journal of Information Science and Engineering, 26(4): 1479-1490
Appears in Collections:[Department and Graduate Institute of Electronic Engineering] Periodical Articles

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