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Maneuvering Target Tracking Based on Unscented Particle Filter Aided by Neutral Network

  • Feng Xue
  • Zhong Liu
  • Zhang-Song Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

A filtering method aided by neural network to improve the maneuvering target tracking performance is proposed in this paper. Based on unscented Kalman filter, the unscented particle filter (UPF) has more accurate proposal distribution and better approximation to non-linear tracking problem than other Sequential Monte-Carlo methods. The neural network is constructed and trained by the maneuvering features, and the outputs of NN are used as acceleration control parameters to correct model parameters. Simulation results show the performance of UPF aided by NN is much improved than extensive Kalman filter.

Keywords

Extensive Kalman Filter Target Tracking Unscented Kalman Filter Neutral Network Maneuvering Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Feng Xue
    • 1
  • Zhong Liu
    • 1
  • Zhang-Song Shi
    • 1
  1. 1.Electronics Engineering CollegeNaval University of EngineeringWuhanChina

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