Mine detection with the Cauchy beam-forming signal-processing approach

Real life sonar applications exist in which impulsive ocean channels tend to produce large- amplitude, short-duration interferences more frequently than Gaussian channels do. The stable law has been shown to successfully model noise over certain impulsive channels. In this paper, we propose new robust techniques for target detection and localization in the presence of noise modeled as a complex isotropic stable process. We develop optimal in the maximum likelihood sense approaches to the direction-of-arrival problem and we introduce the Cauchy Beamformer. We show that the Cauchy Beamformer provides better bearing estimates than the Gaussian Beamformer in a wide range of impulsive noise environments and for very low signal-to-noise ratio values. In addition, we derive the Cramer-Ratio bound on the estimation error covariance for the case of deterministic incoming signals retrieved in the presence of additive complex Cauchy noise. Finally, we demonstrate the robustness of the Cauchy Beamformer via simulation experiments.