Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments  

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作  者:Dah-Jing Jwo Chien-Hao Tseng 

机构地区:[1]Department of Communications,Navigation and Control Engineering,Taiwan Ocean University,Keelung,202301,Taiwan,China [2]National Center for High-Performance Computing,National Applied Research Laboratories,Hsinchu,30076,Taiwan,China

出  处:《Computers, Materials & Continua》2021年第5期1555-1575,共21页计算机、材料和连续体(英文)

基  金:supported by the Ministry of Science and Technology,Taiwan[Grant No.MOST 108-2221-E-019-013]。

摘  要:This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle lter(CPF),which is an estimation algorithm that combines the cubature Kalman lter(CKF)and the particle lter(PF).The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution.It is benecial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems.Based on the spherical-radial transformation to generate an even number of equally weighted cubature points,the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function(pdf)to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’rule.It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system,and thus the importance density function can be used to approximate the true posterior density distribution.In Bayesian ltering,the nonlinear lter performs well when all conditional densities are assumed Gaussian.When applied to the nonlinear/non-Gaussian distribution systems,the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle lterbased approaches,such as the extended particle lter(EPF),and unscented particle lter(UPF),and also the Kalman lter(KF)-type approaches,such as the extended Kalman lter(EKF),unscented Kalman lter(UKF)and CKF.Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.

关 键 词:Nonlinear estimation NON-GAUSSIAN Kalman lter unscented Kalman lter cubature particle filter 

分 类 号:O17[理学—数学]

 

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