Table of Contents
Journal of Computational Engineering
Volume 2014 (2014), Article ID 175820, 6 pages
http://dx.doi.org/10.1155/2014/175820
Research Article

An Improved Unscented Particle Filter with Global Sampling Strategy

Hefei Electronic and Engineering Institute, Hefei 230000, China

Received 13 July 2014; Accepted 22 November 2014; Published 10 December 2014

Academic Editor: Hongli Dong

Copyright © 2014 Yi-zheng Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Particle filter (PF) has many variations and one of the most popular is the unscented particle filter (UPF). UPF uses the unscented Kalman filter (UKF) to generate particles in the PF framework and has a better performance than the standard PF. However, UPF suffers from its high computation complexity because it has to execute UKF to each particle to obtain proposal distribution. This paper gives an improved UPF aiming at reducing the computation complexity of the algorithm. In comparison to the standard UPF, the new strategy generates proposal distribution from the mean and covariance value of the whole particles instead of from each particle. Thus the improved algorithm utilizes the characteristics of the whole particles and only needs to perform UKF algorithm once to get the proposal distribution at each time step. Experimental results show that, compared to standard UPF, the improved algorithm reduces the time consumption greatly almost without performance degradation.