Maximum likelihood estimation for array processing in unknown noise environments

In array signal processing, the spatially white noise model is most commonly used, and most of the high-resolution methods are established on such a noise model. However, in real environments, the noise model is often either unknown or undeterminable. This may cause the high-resolution methods to suffer severe performance degradation. An approach for consistent and parametric direction of arrival (DOA) estimation in unknown noise environments using two separated arrays is proposed under the assumption that noise correlation is spatially limited. This new method can be also applied in radar or sonar tracking and time series analysis.<>