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An Improved Particle Filter Algorithm Based on Neural Network for Target Tracking

  • Qin Wen
  • Peng Qicong
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 228)

Abstract

To the shortcoming of general particle filter, an improved algorithm based on neural network is proposed and is shown to be more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression neural network (GRNN), make them approximate the importance density. Apply the new method to target tracking problem, has made the result more precise than the general particle filter.

Key words

particle filter target tracking general regression neural network 

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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Qin Wen
    • 1
  • Peng Qicong
    • 1
  1. 1.140 Lab, Institution of Communication and Information EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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