Applying image processing and neural network techniques to data association algorithm

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Multiple-target tracking (MTT) is a prerequisite step for radar surveillance systems. Data association is the key technique used in radar MTT systems. This paper presents a new approach for data association that uses both quantity data and image information. In order to combine these two attributes, a fusion algorithm based on the competitive Hopfield neural network (CHNN) is developed to match radar measurements with existing target tracks. When target maneuvering problems are detected, an adaptive maneuvering estimator is applied. Computer simulation results indicate that the proposed approach is suitable for multiple-target tracking problems and has good performance.

Original languageEnglish
Pages (from-to)2427-2439
Number of pages13
JournalInternational Journal of Innovative Computing, Information and Control
Volume7
Issue number5 A
Publication statusPublished - 2011 May 1

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Multiple Target Tracking
Data Association
Target tracking
Radar
Image Processing
Image processing
Neural Networks
Neural networks
Surveillance radar
Hopfield neural networks
Radar measurement
Hopfield Neural Network
Target
Tracking System
Surveillance
Fusion
Fusion reactions
Computer Simulation
Attribute
Estimator

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

Cite this

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abstract = "Multiple-target tracking (MTT) is a prerequisite step for radar surveillance systems. Data association is the key technique used in radar MTT systems. This paper presents a new approach for data association that uses both quantity data and image information. In order to combine these two attributes, a fusion algorithm based on the competitive Hopfield neural network (CHNN) is developed to match radar measurements with existing target tracks. When target maneuvering problems are detected, an adaptive maneuvering estimator is applied. Computer simulation results indicate that the proposed approach is suitable for multiple-target tracking problems and has good performance.",
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