Array Processing Based on GARCH Model

In this paper, we propose a new model for additive noise based on GARCH time-series in arraysignal processing. Due to the some reasons such as complex implementation and computational problems, probability distribution function of additive noise is assumed Gaussian. In the different applications, scrutiny and measurement of noise shows that noise can sometimes significantly non-Gaussian and thus the methods based on Gaussian noise will degrade in an actual conditions. Heavy-tail probability density function (PDF) and time-varying statistical characteristics (e.g.; variance) are the most features of the additive noise process. On the other hand, GARCH process has important properties such as heavy-tail PDF (as excess kurtosis) and volatility modeling through feedback mechanism onto conditional variance so that it seems the GARCH model is a good candidate for the additive noise model in the array processing applications. In this paper, we propose a new method based on GARCH using the maximum likelihood approach in array processing and verify the performance of this approach in the estimation of the Direction-of-Arrivals of sources against the other methods and using the Cramer-Rao Bound.