Multiple-Target Tracking with Competitive Hopfield Neural Network Based Data Association

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...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2007-07, Vol.43 (3), p.1180-1188
Main Authors: Chung, Yi-Nung, Chou, Pao-Hua, Yang, Maw-Rong
Format: Article
Language:English
Subjects:
Aircraft components
Computer simulation
Cost function
Electronic systems
Hopfield neural networks
Learning
Neural networks
Neurons
Partitioning algorithms
Radar measurement
Radar measurements
Radar tracking
Surveillance
Target tracking
Tracking
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Summary: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.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2007.4383609