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Journal of Applied Mathematics
Volume 2013, Article ID 302170, 7 pages
Research Article

Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

1Shandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2School of Control Science and Engineering, Shandong University, Jinan 250061, China
3State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100090, China

Received 12 April 2013; Revised 22 July 2013; Accepted 30 July 2013

Academic Editor: Debasish Roy

Copyright © 2013 Zeng-Shun Zhao et al. 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.


The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF could not only improve the estimation precision but also reduce the overall computational complexity. However, the computational burden is still too high for many real-time applications. To improve the efficiency of RBPF, the particle swarm optimization (PSO) is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. So only a small number of particles are needed to participate in the required computation. The experimental results demonstrate that this novel algorithm is more efficient than the standard RBPF.