An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification
Abstract
This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the output data is exploited in this work to derive an expectation-maximization (EM) algorithm for parameter estimation of the state-space and noise covariance matrices. For this purpose, we use the incremental discrete-time equivalent of the system, which leads to EM iterations of the continuous-time state-space matrices that can be computed by standard filtering and smoothing procedures. The effectiveness of the identification method is tested via Monte Carlo simulations.
Cite
@article{arxiv.2304.03314,
title = {An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification},
author = {Rodrigo A. González and Angel L. Cedeño and María Coronel and Juan C. Agüero and Cristian R. Rojas},
journal= {arXiv preprint arXiv:2304.03314},
year = {2023}
}
Comments
6 pages, 3 figures