Original title: Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
Authors: Vlk, T. ; Dedecius, Kamil
Document type: Research reports
Year: 2022
Language: eng
Series: Research Report, volume: 2395
Abstract: 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.
Keywords: Collaborative estimation; State estimation; Variational Bayesian methods

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2022/AS/dedecius-0562434.pdf
Original record: https://hdl.handle.net/11104/0334861

Permalink: http://www.nusl.cz/ntk/nusl-510677


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