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 journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1180-1188
Number of pages9
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume43
Issue number3
DOIs
Publication statusPublished - 2007 Jul 1

Fingerprint

Hopfield neural networks
Target tracking
Radar measurement
Learning algorithms
Radar
Computer simulation

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

Cite this

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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.",
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Multiple-target tracking with competitive Hopfield neural network based data association. / Chung, Yi-Nung; Chou, Pao Hua; Yang, Maw Rong; Chen, Hsin Ta.

In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 3, 01.07.2007, p. 1180-1188.

Research output: Contribution to journalArticle

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