English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6491/11663
Visitors : 24503267      Online Users : 73
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11745

Title: Mulitple-Target Tracking with Competitive Hopfield Neural Network-based Data Association
Authors: Chung, Yi-Nung;Chou, Pao-Hua;Yang, Maw-Rong;Chen, Hsin-Ta
Contributors: 電機工程學系
Date: 2007-07
Issue Date: 2012-07-02T02:06:51Z
Publisher: IEEE
Abstract: Data association which obtains relationship between radar measurements and existing tracks plays one important role in
radar multiple-target tracking (MTT) systems. A new approach to data association based on the competitive Hopfield neural
network (CHNN) is investigated, where the matching between radar measurements and existing target tracks is used as a
criterion to achieve a global consideration. Embedded within the CHNN is a competitive learning algorithm that resolves the
dilemma of occasional irrational solutions in traditional Hopfield neural networks. Additionally, it is also shown that our proposed
CHNN-based network is guaranteed to converge to a stable state in performing data association and the CHNN-based data
association combined with an MTT system demonstrates target tracking capability. Computer simulation results indicate that this
approach successfully solves the data association problems.
Relation: IEEE Trans. Aerosp.Electron. Syst, 43(3): 1180-1188
Appears in Collections:[電機工程學系] 期刊論文

Files in This Item:

File SizeFormat
index.html0KbHTML493View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback