Fixed Parameter Estimation Method Using Gaussian Particle Filter

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


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jian, K., Si, X.C., Rui, G.S.: Particle Filtering Techniques Based on Bayesian Theorem. Modern Radar. 26, 34–36 (2004)Google Scholar
  2. 2.
    Carpenter, J., Clifford, P., Fearnhead, P.: Improved Particle Filter for Nonlinear Problems. IEE Proc. Radar Sonar Navig., 146 (1999)Google Scholar
  3. 3.
    Sanjeev, M., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Processing 50, 174–188 (2002)CrossRefGoogle Scholar
  4. 4.
    Miguez, J., Bugallo, F., Djuric, M.: Novel Particle Filtering Algorithm for Fixed Parameter Estimation in Dynamic Systems. In: Proceedings of The 4th International Symposium on Image and Signal Processing and Analysis, pp. 46–51 (2005)Google Scholar
  5. 5.
    Herman, S., Moulin, P.: A Particle Filtering Approach to FM-Band Passive Radar Tracking and Automatic Target Recognition. In: IEEE Aerospace Conference Proceedings, pp. 1789–1808 (2002)Google Scholar
  6. 6.
    Karlsson, R., Gustafsson, F.: Particle Filter for Underwater Terrain Navigation. In: IEEE Workshop on Statistical Signal Processing, pp. 526–529 (2003)Google Scholar
  7. 7.
    Karlsson, R., Gusfafsson, F., Karlsson, T.: Particle Filtering and Cramer-Rao Lower Bound for Underwater Navigation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp v–65–68 (2003)Google Scholar
  8. 8.
    Kotecha, H., Djuric, M.: Gaussian Particle Filtering. IEEE Trans. on Signal Processing 51, 2592–2601 (2003)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

Personalised recommendations