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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Main Authors:	Zeng-Shun Zhao, Xiang Feng, Yan-yan Lin, Fang Wei, Shi-Ku Wang, Tong-Lu Xiao, Mao-Yong Cao, Zeng-Guang Hou, Min Tan
Format:	Article
Language:	English
Published:	Hindawi Limited 2013-01-01
Series:	Journal of Applied Mathematics
Online Access:	http://dx.doi.org/10.1155/2013/302170

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spelling	doaj-9c43a5dbf61144168f0f5a7c794dc2db2020-11-24T23:04:32ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/302170302170Improved Rao-Blackwellized Particle Filter by Particle Swarm OptimizationZeng-Shun Zhao0Xiang Feng1Yan-yan Lin2Fang Wei3Shi-Ku Wang4Tong-Lu Xiao5Mao-Yong Cao6Zeng-Guang Hou7Min Tan8Shandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Province Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100090, ChinaState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100090, ChinaThe 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.http://dx.doi.org/10.1155/2013/302170
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Zeng-Shun Zhao Xiang Feng Yan-yan Lin Fang Wei Shi-Ku Wang Tong-Lu Xiao Mao-Yong Cao Zeng-Guang Hou Min Tan
spellingShingle	Zeng-Shun Zhao Xiang Feng Yan-yan Lin Fang Wei Shi-Ku Wang Tong-Lu Xiao Mao-Yong Cao Zeng-Guang Hou Min Tan Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization Journal of Applied Mathematics
author_facet	Zeng-Shun Zhao Xiang Feng Yan-yan Lin Fang Wei Shi-Ku Wang Tong-Lu Xiao Mao-Yong Cao Zeng-Guang Hou Min Tan
author_sort	Zeng-Shun Zhao
title	Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
title_short	Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
title_full	Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
title_fullStr	Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
title_full_unstemmed	Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
title_sort	improved rao-blackwellized particle filter by particle swarm optimization
publisher	Hindawi Limited
series	Journal of Applied Mathematics
issn	1110-757X 1687-0042
publishDate	2013-01-01
description	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.
url	http://dx.doi.org/10.1155/2013/302170
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Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

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