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Fixed Parameter Estimation Method Using Gaussian Particle Filter

  • Lixin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

The degeneracy problems of general particle filtering frequently occur. Although this kind of problems can be mitigated by resampling, but the diversity characteristic between particles may be lost because the higher weighted particles will be replicated and the lower weighted particles will be discarded. For parameter-fixed application cases, the standard particle filter is invalid as no importance density function can be sampled for new particles required by the predictive distribution, and particles will quickly be exhausted. This paper proposes a new method for the parameter-fixed estimation by use of Gaussian particle filter, which can avoid making particles exhausted and can improve the estimation performance. Refer to a practical example of Direction of Arrived (DOA) estimation for coherent signals propagated in space with multi-path fading, the computer simulation has been performed. The simulation results have indicated that the performance of the new method is rather than general particle filtering.

Keywords

Particle Filter Extend Kalman Filter Coherent Signal Posterior Probabilistic Density Fixed Parameter Estimation 
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

  • Lixin Wang
    • 1
  1. 1.School of Communication EgineeringHangzhou Dianzi UniversityHangzhou, Zhejiang ProvinceChina

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