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Multiple-Target Tracking with Competitive Hopfield Neural Network Based Data Association

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4 Author(s)
Yi-Nung Chung ; Nat. Changhua Univ. of Educ., Changhua ; Pao-Hua Chou ; Maw-Rong Yang ; Hsin-Ta Chen

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.

Published in:

IEEE Transactions on Aerospace and Electronic Systems  (Volume:43 ,  Issue: 3 )