Multiple-target tracking with competitive Hopfield neural network based data association

Yi Nung Chung, Pao Hua Chou, Maw Rong Yang, Hsin Ta Chen

研究成果: Article同行評審

23 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)1180-1188
頁數9
期刊IEEE Transactions on Aerospace and Electronic Systems
43
發行號3
DOIs
出版狀態Published - 2007 七月 1

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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