Array signal processing using GARCH noise modeling

We propose a new method for modeling practical non-Gaussian and non-stationary noise in array signal processing. GARCH (generalized autoregressive conditional heteroscedasticity) models are introduced as the feasible model for the heavy tailed probability density functions (PDFs) and time varying variances of stochastic processes. We use the GARCH noise model in the maximum likelihood approach for the estimation of directions-of-arrival (DOAs). Our analysis exploits time varying variance and spatially non-uniform noise in sensor array signal processing. We show through simulations that this GARCH modeling is suitable for high-resolution source separation and noise suppression in a non-Gaussian environment.