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Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
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SYSNO ASEP 0562434 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Diffusion Kalman filtering under unknown process and measurement noise covariance matrices Author(s) Vlk, T. (CZ)
Dedecius, Kamil (UTIA-B) RID, ORCIDNumber of authors 2 Issue data Praha: ÚTIA AV ČR, v. v. i.,, 2022 Series Research Report Series number 2395 Number of pages 29 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords Collaborative estimation ; State estimation ; Variational Bayesian methods Subject RIV IN - Informatics, Computer Science OECD category Automation and control systems Institutional support UTIA-B - RVO:67985556 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2023
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