吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1435-1440.doi: 10.7964/jdxbgxb201405034

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Improved UPF algorithm based on Gaussian Sigma points selection

CAO Jie, DAI Bin, LI Xiao-xu   

  1. College of Computer and Communication,Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2013-03-07 Online:2014-09-01 Published:2014-09-01

Abstract: An improved Unscented Particle Filter (UPF) algorithm is proposed to overcome the problem of particles degradation in the standard particle filter. The algorithm uses adaptive unscented Kalman filter based on Gaussian Sigma point selection to generate the proposal distribution function. Then it uses the Metropolis-Hastings (MH) algorithm to optimize particles, so that the approximation of the posterior probability density of the system is improved. Simulation results show that the improved UPF algorithm reduces particle degradation which exists in the particle filter algorithm, and improves tracking accuracy.

Key words: computer application, particle filter, Gaussian Sigma points, unscented Kalman filter, Metropolis-Hastings

CLC Number: 

  • TP391
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