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

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

doi:10.1109/TAES.2007.4383609

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

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

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

23 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 language	English
Pages (from-to)	1180-1188
Number of pages	9
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	43
Issue number	3
DOIs	https://doi.org/10.1109/TAES.2007.4383609
Publication status	Published - 2007 Jul

All Science Journal Classification (ASJC) codes

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2007.4383609

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title = "Multiple-target tracking with competitive Hopfield neural network based data association",

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.",

author = "Chung, {Yi Nung} and Chou, {Pao Hua} and Yang, {Maw Rong} and Chen, {Hsin Ta}",

note = "Funding Information: This work was supported by the United States Office of Naval Research (ONR 31). Funding Information: This work was supported by the National Science Council under Grant NSC95-2221-E-212-021.",

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AU - Chou, Pao Hua

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AU - Chen, Hsin Ta

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