Adaptive array processing in non-Gaussian environments

In several adaptive array application areas the Gaussian distribution has not proven to be an accurate model of the measured data. Nevertheless, Gaussian based processors have demonstrated robust performance in spite of this statistical mismatch. A need therefore exists for the consideration of (i) problem reformulation and (ii) performance analysis in non-Gaussian environments. The theory of complex multivariate elliptically contoured (MEC) distributions provides an attractive theoretic framework for these considerations especially in the adaptive array setting. We replace the Gaussian data assumption with one of MEC distributed and reexamine the optimality and performance of widely used adaptive detection and beamforming structures.