Finite Impulse Response Errors-in-Variables system identification utilizing Approximated Likelihood and Gaussian Mixture Models

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Cedeno, Angel L., Orellana, Rafael, Carvajal, Rodrigo, Godoy, Boris I., Aguero, Juan C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Control Engineering; Electrical Engineering, Electronic Engineering, Information Engineering; Elektroteknik och elektronik; Engineering and Technology; Errors; Errors-in-Variables; Estimation; Expectation-Maximization; Finite impulse response filters; Gaussian Mixture distribution; Gaussian mixture model; Impulse response; Maximum Likelihood; Maximum likelihood estimation; Measurement uncertainty; Noise measurement; Parameters; Probabilistic models; Probability density function; Reglerteknik; System identification; Teknik
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3255827

In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed. We propose an Expectation-Maximization-based algorithm to estimate the system model parameters, the input and output noise variances, and the Gaussian mixture noise-free input parameters. The benefits of our proposal are illustrated via numerical simulations.