Comparison of Nonlinear Filtering Algorithms Based on Sampling
Abstract：In dealing with real-time estimation of dynamic system,such as target tracking.The extended Kalman filter（EKF） is used as a state estimation method to improve the estimation accuracy.However,there is estimation error in linearizing system due to the defects of EKF in nonlinear estimation,which affects the accuracy of target tracking.Three new nonlinear filter algorithms are presented in order to yield higher estimation accuracy.The three methods are unscented Kalman filter（UKF） and particle filter（PF） and UPF.the algorithms are analyzed.The applications of the algorithms to the state estimation models are compared.Finally,the algorithms are compared through a tracking model simulation.Experiment results show that the proposed algorithms outperforms EKF at convergence speed,consistency and tracking precision.
Keywords：unscented Kalman filterparticle filterunscented particle filternonlineartarget tracking
Publication Date：2012-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 31-32,50 )