Quadrature Kalman particle fitler  

作　　者：Chunlincl Wu Chongzhao Han 

机构地区：[1]School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, P. R. China [2]School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第2期175-179,共5页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(60574033)

摘　　要：In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.

关 键 词：particle filter statistical linear regression quadrature Kalman filter importance density function. 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] TN713[自动化与计算机技术—控制科学与工程]