Applying likelihood on Hopfield neural network for radar tracking

Yi-Nung Chung, Maw Rong Yang, Dend Jyi Juang, Tsung Chun Hsu, Shun Peng Hsu

Research output: Contribution to journalArticle

Abstract

The multiple-target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.

Original languageEnglish
Pages (from-to)339-342
Number of pages4
JournalJournal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
Volume31
Issue number2
DOIs
Publication statusPublished - 2008 Jan 1

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Radar tracking
Hopfield neural networks
Radar systems
Target tracking
Radar measurement
Neural networks
Computer simulation

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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abstract = "The multiple-target tracking (MTT) algorithm plays an important role in radar systems. Data association is the most important technique to solve the tracking problems associating dense measurements with existing tracks. A new approach applying Likelihood to measurements and existing tracks in a radar system based on Neural Network computation is investigated in this paper. The proposed algorithm will solve both the data association and the target tracking problems simultaneously. With this approach, the matching between radar measurements and existing target tracks can achieve global relevance. Computer simulation results indicate the ability of this algorithm to keep track of targets under various conditions.",
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Applying likelihood on Hopfield neural network for radar tracking. / Chung, Yi-Nung; Yang, Maw Rong; Juang, Dend Jyi; Hsu, Tsung Chun; Hsu, Shun Peng.

In: Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an, Vol. 31, No. 2, 01.01.2008, p. 339-342.

Research output: Contribution to journalArticle

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