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|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|
|Issue Date: ||2012-07-02T02:06:51Z
|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|
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