An intelligent particle filter with hybrid adaptive resampling
ZHANG Xin-yu
REN Meng-jiao
YI Ying-min
ZHANG Zi-yue
WU Shu-yue
Abstract：Particle filter(PF)has good estimation performance for nonlinear and non-Gaussian systems,but the lack of particle diversity has always been the vital problem affecting the estimation accuracy of PF after the introduction of resampling technology.Therefore,an intelligent PF method based on hybrid adaptive resampling is proposed.Firstly,a function of computing the covariance matrix adaptively for Gaussian variation is designed in this method on the basis of hybrid adaptive Metropolis-Hastings(M-H)resampling.Secondly,an acceptance and rejection criterion function using the mode of survival of the fittest is developed.Finally,the effective particle set is updated in real time to improve the quality of the particle set and the accuracy of the PF.Two one-dimensional nonlinear models and one high-dimensional nonlinear model are used to verify the effectiveness of the proposed method.Experimental results show that the proposed method can effectively improve the quality of the particle after resampling and improve the estimation accuracy of the PF compared with the existing resampling methods.
Keywords：information theory and signal processingstate estimationparticle filterM-H resamplingGaussian variationadaptive variance
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：9( 348-356 )