학술논문
Linear Time-Varying Parameter Estimation: Maximum A Posteriori Approach via Semidefinite Programming
Document Type
Periodical
Author
Source
IEEE Control Systems Letters IEEE Control Syst. Lett. Control Systems Letters, IEEE. 8:73-78 2024
Subject
Language
ISSN
2475-1456
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.