Diffusion Kalman filtering under unknown process and measurement noise covariance matrices

0562434 - ÚTIA 2023 CZ eng V - Research Report
Vlk, T. - Dedecius, Kamil
Diffusion Kalman filtering under unknown process and measurement noise covariance matrices.
Praha: ÚTIA AV ČR, v. v. i.,, 2022. 29 s. Research Report, 2395.
Institutional support: RVO:67985556
Keywords : Collaborative estimation * State estimation * Variational Bayesian methods
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2022/AS/dedecius-0562434.pdf

The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the process and measurement noise covariance matrices are known and properly set. This is a relatively strong assumption in the signal processing domain. By design, the Kalman filters are rather sensitive to its violation, which may potentially lead to their divergence. In this paper, we propose a novel distributed filtering algorithm with increased robustness under unknown process and measurement noise covariance matrices. It is formulated as a Bayesian variational message passing procedure for simultaneous analytically tractable inference of states and measurement noise covariance matrices.
Permanent Link: https://hdl.handle.net/11104/0334861