Fractional central difference Kalman filter with unknown prior information

被引：33

作者：

Liu, Tianyu ^{[1
]}

Cheng, Songsong ^{[1
]}

Wei, Yiheng ^{[1
]}

Li, Ang ^{[1
]}

Wang, Yong ^{[1
]}

机构：

[1] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China

来源：

SIGNAL PROCESSING | 2019年 / 154卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Fractional calculus; Adaptive filter; Fractional Kalman filter; Central difference Kalman filter; Maximum a posteriori principle; STATE ESTIMATION; ORDER SYSTEMS;

D O I：

10.1016/j.sigpro.2018.08.006

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a generalized fractional central difference Kalman filter for nonlinear discrete fractional dynamic systems is proposed. Based on the Stirling interpolation formula, the presented algorithm can be implemented as no derivatives are needed. Besides, in order to estimate the state with unknown prior information, a maximum a posteriori principle based adaptive fractional central difference Kalman filter is derived. The adaptive algorithm can estimate the noise statistics and system state simultaneously. The unbiasedness of the proposed algorithm is analyzed. Several numerical examples demonstrate the accuracy and effectiveness of the two Kalman filters. (C) 2018 Published by Elsevier B.V.

引用

页码：294 / 303

页数：10

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