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

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

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1180-1188
Number of pages9
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number3
Publication statusPublished - 2007 Jul

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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