Robust Covariance Matrix Estimation for Uniform Rectangular Array

Covariance matrix estimation is a fundamental component of adaptive signal processing methods. Motivated by the data structure brought by uniform rectangular arrays, we address the problem of block Toeplitz structure covariance estimation in non-Gaussian environments. We propose a robust estimator that incorporates structural constraints on the covariance matrix, enhancing resilience to deviations from Gaussian assumptions. The performance of the proposed approach is assessed with respect to the Cramér-Rao bound. A direction-ofarrival estimation for uniform rectangular arrays is presented to demonstrate the effectiveness of the framework in source localization tasks.