Maximum Likelihood Localization using GARCH Noise Models

In this paper we propose a new source localization method using additive noise modeling based on generalized autoregressive conditional heteroscedasticity (GARCH) time-series. We use the GARCH noise model in the maximum likelihood (ML) sense for the estimation of direction of arrival (DOA) of impinging narrowband sources. In an actual application, the measurement of additive noise in a natural environment shows that noise can sometimes be significantly non-Gaussian and non-stationary. GARCH time-series are feasible for heavy-tail probability density function (PDF) and time-varying variances of stochastic noise process. We examine the suitability of the proposed method using simulated and experimental data