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arXiv · 2304.03314

An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification

Rodrigo A. González·Angel L. Cedeño·María Coronel·Juan C. Agüero·Cristian R. Rojas
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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.

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Rodrigo A. González, Angel L. Cedeño, María Coronel, Juan C. Agüero, Cristian R. Rojas. 2023-04-06. An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification. https://arxiv.org/abs/2304.03314

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