Computer Science ›› 2014, Vol. 41 ›› Issue (6): 199-203.doi: 10.11896/j.issn.1002-137X.2014.06.039

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Cost Reference Marginalized Particle Filter Based on Adaptive Particle Swarm Optimization

HU Zhen-tao,WEI Dan,JIN Yong and HU Yu-mei   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Aiming at the precise measures of important weights and the effective sampling of particle in measurement uncertainty,a novel cost reference marginalized particle filter based on adaptive particle swarm optimization was proposed.In the new algorithm,cost function and risk function are firstly introduced to complete reasonable utilization of the latest observation,and the dependency on priori information of observation noise in classical measuring method of important weights is improved.Secondly,through the extraction and utilization of particle distribution features information,the adaptive selection strategy of the limit velocity is obtained and a new adaptive particle swarm optimization method is given.Finally,combining with the mechanism of colony optimization in particle swarm optimization,the approximation effectiveness of sampling particles relative to estimated state is enhanced,and the diversity of particle after re-sampling is improved.The theoretical analysis and experimental results show the efficiency of the proposed algorithm.

Key words: Nonlinear filter,Marginalized particle filter,Particle swarm optimization,Measurement uncertainty

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