Maximum likelihood localization of sources in noise modeled as a Cauchy process

A new robust beamformer, based on the Cauchy additive noise assumption, is introduced. The maximum likelihood approach is used for the bearing estimation of multiple sources from a set of snapshots when the interference is impulsive in nature. It is shown that the Cauchy receiver greatly outperforms the Gaussian receiver in a wide variety of non-Gaussian noise environments, and performs comparably to the Gaussian receiver when the additive noise is Gaussian. The Cramer-Rao bound on the estimation error variance is derived, and the robustness of the Cauchy beamformer in a wide range of impulsive interference environments is demonstrated via simulation experiments