A novel variational Bayesian adaptive Kalman filter with mismatched process noise covariance matrix

Autor: Xinrui Liu, Hong Xu, Daikun Zheng, Yinghui Quan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Radar, Sonar & Navigation, Vol 17, Iss 6, Pp 967-977 (2023)
Druh dokumentu: article
ISSN: 1751-8792
1751-8784
DOI: 10.1049/rsn2.12391
Popis: Abstract This paper proposes a novel variational Bayesian (VB) adaptive Kalman filter with mismatched process noise covariance matrix (PNCM). Firstly, this paper explains the reason why the predicted error covariance matrix (PECM) is chosen for variational inference. Secondly, compared with the earlier VB adaptive Kalman filter (VB‐AKF‐Q), the proposed filter calculate the dynamic model of the PECM with its historical estimation information. Therefore, the proposed filter can overcome the influence of mismatched PNCM on the initial value setting of PECM in the VB‐AKF‐Q. Finally, we use the evidence lower bound for the proposed filter and give the convergence criterion on this basis. Some examples with a target tracking simulation are carried out to demonstrate the superiority of the proposed filter.
Databáze: Directory of Open Access Journals
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