Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

Angel L. Cedeno, Rafael Orellana, Rodrigo Carvajal, Boris I. Godoy, Juan C. Aguero

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)24615-24630
Número de páginas16
PublicaciónIEEE Access
Volumen11
DOI
EstadoPublicada - 2023

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