The dual-kalman filtering and neural solutions to maneuvering estimation problems

Y. I.Nung Chung, Dend Jyi Juang, Kuo Chang Hu, Ming Liang Li, Kai Chih Chuang

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Abstract

Tracking maneuvering targets in a radar system is m ore complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the m aneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the m aneuvering targets' tracking problems. In this approach, two collaborativ e Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of m aneuver. Based on this approach, the m ost approximate target's acceleration can be detected and estimated in real tim e. Moreover, it is also shown that one Com petitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability.

Original languageEnglish
Pages (from-to)1479-1490
Number of pages12
JournalJournal of Information Science and Engineering
Volume26
Issue number4
Publication statusPublished - 2010 Jul 1

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All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

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