Linear Time-Varying Parameter Estimation
Maximum A Posteriori Approach via Semidefinite Programming
Sasan Vakili (TU Delft - Team Manuel Mazo Jr)
Mohammad Khosravi (TU Delft - Team Khosravi)
Peyman Mohajerin Esfahani (TU Delft - Team Peyman Mohajerin Esfahani)
Manuel Mazo (TU Delft - Team Manuel Mazo Jr)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.