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

Abstract

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.

Keywords

• Errors-in-variables
• maximum likelihood
• expectation-maximization
• Gaussian mixture distribution
• estimation