Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

Version 1 : Received: 6 December 2020 / Approved: 7 December 2020 / Online: 7 December 2020 (14:54:14 CET)

A peer-reviewed article of this Preprint also exists.

Shan, C.; Zhou, W.; Yang, Y.; Jiang, Z. Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian. Sensors 2021, 21, 198. Shan, C.; Zhou, W.; Yang, Y.; Jiang, Z. Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian. Sensors 2021, 21, 198.

Journal reference: Sensors 2020, 21, 198
DOI: 10.3390/s21010198

Abstract

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

Subject Areas

variational Bayesian; multiple-fading factors; time-varying noise covariance matrices; inaccurate noise; target tracking; update monitoring strategy

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