Title
Linear Time-Varying Parameter Estimation: Maximum A Posteriori Approach via Semidefinite Programming
Author
Vakili, S. (TU Delft Team Manuel Mazo Jr)
Khosravi, M. (TU Delft Team Khosravi)
Mohajerin Esfahani, P. (TU Delft Team Peyman Mohajerin Esfahani) 
Mazo, M. (TU Delft Team Manuel Mazo Jr)
Date
2024
Abstract
We study the problem of identifying a linear time-varying output map from measurements and linear time-varying system states, which are perturbed with Gaussian observation noise and process uncertainty, respectively. Employing a stochastic model as prior knowledge for the parameters of the unknown output map, we reconstruct their estimates from input/output pairs via a Bayesian approach to optimize the posterior probability density of the output map parameters. The resulting problem is a non-convex optimization, for which we propose a tractable linear matrix inequalities approximation to warm-start a first-order subsequent method. The efficacy of our algorithm is shown experimentally against classical Expectation Maximization and Dual Kalman Smoother approaches.
Subject
Estimation
identification
linear matrix inequalities
optimization
semidefinite programming
To reference this document use:
http://resolver.tudelft.nl/uuid:421e00ec-e147-41b0-9c3b-2a14cf41c4d6
DOI
https://doi.org/10.1109/LCSYS.2023.3347198
Embargo date
2024-06-25
ISSN
2475-1456
Source
IEEE Control Systems Letters, 8, 73-78
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 S. Vakili, M. Khosravi, P. Mohajerin Esfahani, M. Mazo