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

arXiv: https://arxiv.org/abs/2304.03314 · HTML: https://mlsanity.com/paper/2304.03314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [System Identification for Continuous-time Linear Dynamical Systems](https://mlsanity.com/paper/2308.11933/) (2023)

## Abstract (arXiv)

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.