Robust object identification in the presence of non-Gaussian interference

Abstract

The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.