A Novel Fractional Kalman Filter Algorithm With Noisy Input

Xinyu Zhang, Keyi Fan, Wentao Ma, Jiemin Duan, Junli Liang, Ruirui Ji

科研成果: 期刊稿件文章同行评审

11 引用 (Scopus)

摘要

For a fractional order system (FOS) affected by input noise, the result of general fractional Kalman filter (GFKF) is biased. To overcome this, this brief proposes a new fractional Kalman filter (FKF) algorithm considering input noise. Firstly, it is proved that the result of the GFKF method is biased when the input vector includes the noise. Secondly, we redefine the criterion function of the error of state estimation during the derivation process of the FKF, in which a term about the input noise is added into the covariance matrix during the prior-estimation. Then an improved covariance matrix and Kalman gain are gotten, respectively. Due to the consideration of the input noise, this method can remove the error caused by the input noise. Experiment results illustrate that the algorithm of this brief has superior performance for systems with input noise compared with the GFKF method.

源语言英语
页(从-至)1239-1243
页数5
期刊IEEE Transactions on Circuits and Systems II: Express Briefs
70
3
DOI
出版状态已出版 - 1 3月 2023

指纹

探究 'A Novel Fractional Kalman Filter Algorithm With Noisy Input' 的科研主题。它们共同构成独一无二的指纹。

引用此