Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

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 al...

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Bibliographic Details
Published in:Journal of Applied Mathematics 2013-01, Vol.2013 (2013), p.822-828-196
Main Authors: Hou, Zeng-Guang, Tan, Min, Zhao, Zeng-Shun, Cao, Mao-Yong, Xiao, Tong-Lu, Wang, Shi-Ku, Feng, Xiang, Lin, Yan-yan, Wei, Fang
Format: Article
Language:English
Subjects:
Algorithms
Computation
Estimates
Filters (Mathematics)
Mathematical analysis
Mathematical models
Mathematical optimization
Mathematical research
Real time
Swarm intelligence
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Summary: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.
ISSN:1110-757X
1687-0042
DOI:10.1155/2013/302170