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Optimized Extended Kalman Filter Based on Learning Kernel Partial Least Squares

doi:10.3969/j.issn.1006-2475.2018.09.021

Abstract

Abstract: To solve the problem that the error of EKF estimation is larger with time sequence caused by uncertain parameters and inaccuracy noise information of none-liner system, the character of kernel partial least square which is independent of system equation parameters and noise information is used to optimize EKF. Firstly, measurement and convergence estimation value are as studying samples to build prediction model. And then, prediction values of KPLS and EKF are fused to estimate system status. Mean while, if convergence criterion of status estimation is true, then estimation values are as studying samples, and the sliding window is used to update kernel matrix, which makes KPLS have the ability to be predicted with time sequence. Else if convergence criterion of status estimation is false, then the measurement covariance will be updated. Finally, convergence and performance of KPLS-EKF are analyzed. The experimental results show that the proposed problem can be effectively solved by KPLS-EKF.

Key words: non-linear, kernel partial least square, extended Kalman filter, convergence criterion

CLC Number:

TP391

BAI Xiao-bo1, SHAO Jing-feng1, HE Zheng1, TIAN Jian-gang2. #br# Optimized Extended Kalman Filter Based on Learning Kernel Partial Least Squares[J]. Computer and Modernization, 2018, 0(09): 110-.

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#br# Optimized Extended Kalman Filter Based on Learning Kernel Partial Least Squares

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