Robust adaptive detection with angular rejection

This paper deals with robust adaptive detection of a useful target in the presence of interfering signals. The background environment is assumed homogeneous and Gaussian with unknown covariance matrix. At the design stage, we devise a robust receiver with angular rejection capabilities, accounting for covariance and steering uncertainties. We prove that the maximization of the concentrated likelihood function shares a hidden convexity property. Specifically, exploiting some recent results concerning trigonometric polynomials, we formulate the apparently nonconvex optimization over the phase as a Semidefinite Programming convex optimization problem. At the analysis stage, we assess the performance of the new receiver in comparison with classic detectors available in open literature.