Adaptive fractional‐order unscented Kalman filter with unknown noise statistics. (28th July 2022)

• Record Type: Journal Article
• Authors: Xiao, Kui; Yu, Wentao; Qu, Feng; Lian, Jianfang; Liu, Chaofan; Liu, Weirong
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 10(2022)
• Page Start: 2519
• Page End: 2536
• Publication Date: 2022-07-28
• Subjects: fractional‐order system -- maximum a posterior principle -- short‐term memory effect -- Unscented Kalman filter; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3472
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article deals with state estimation of complex nonlinear discrete fractional‐order systems with unknown noise statistics by means of an adaptive fractional‐order Unscented Kalman filter (AFUKF). Firstly, in order to alleviate the communication burden of fractional‐order Unscented Kalman filter, short‐term memory effect is utilized to decide an appropriate memory length. Then aiming at the problem of filtering divergence and accuracy degradation caused by unknown statistical characteristics of noise, based on the maximum a posterior (MAP) principle, a noise statistical estimator is introduced to estimate and correct the statistical characteristics of noise in real‐time. Finally, the unbiasedness of the proposed algorithm is analyzed to verify that the estimated mean and covariance of noise are unbiased. The effectiveness and accuracy of AFUKF are demonstrated via simulation experiments.