Paper Title:
Compressed Iterative Particle Filter for Target Tracking
  Abstract

Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep the variety of the particles. Simuational results have shown that the proposed algorithm has better performances than other three typical filtering algorithms.

  Info
Periodical
Edited by
Qi Luo
Pages
91-94
DOI
10.4028/www.scientific.net/AMM.55-57.91
Citation
H. B. Zhu, H. Zhao, D. Liu, C. H. Song, "Compressed Iterative Particle Filter for Target Tracking", Applied Mechanics and Materials, Vols. 55-57, pp. 91-94, 2011
Online since
May 2011
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