Open AccessImproved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
Author(s) -
Zeng-Shun Zhao,
Xiang Feng,
Yanyan Lin,
Wei Fang,
Shi-Ku Wang,
Tong-Lu Xiao,
Maoyong Cao,
ZengGuang Hou,
Min Tan
Publication year - 2013
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2013/302170
Subject(s) - particle filter , particle swarm optimization , computation , nonlinear system , particle (ecology) , computer science , algorithm , filter (signal processing) , mathematical optimization , particle number , control theory (sociology) , mathematics , artificial intelligence , physics , biology , ecology , control (management) , quantum mechanics , computer vision , plasma
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
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