A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation
To resolve the problems of particle impoverishment and sample size dependency, particle swarm optimization (PSO) is introduced into generic particle filter (PF). This novel method, particle swarm optimized particle filter (PSOPF), incorporates the newest observations into sampling process and also o...
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| creator | Guofeng Tong Zheng Fang Xinhe Xu |
| description | To resolve the problems of particle impoverishment and sample size dependency, particle swarm optimization (PSO) is introduced into generic particle filter (PF). This novel method, particle swarm optimized particle filter (PSOPF), incorporates the newest observations into sampling process and also optimizes that process. Through particle swarm optimization, particle samples are moved towards regions where particles have larger values of posterior density function. As a result, the impoverishment of particle filter is overcome and the sample size necessary for accurate state estimation is reduced dramatically. Two experiments show the validation of our method. |
| doi_str_mv | 10.1109/CEC.2006.1688342 |
| format | Conference Proceeding |
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| ispartof | 2006 IEEE International Conference on Evolutionary Computation, 2006, p.438-442 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Computational efficiency Monte Carlo methods Nonlinear systems Particle filters Particle swarm optimization Probability distribution Proposals Sampling methods State estimation |
| title | A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation |
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