CS versus MAP and MMOSPA for multi-target radar AOAs

We compare Minimum Mean Optimal Sub-Pattern Assignment error (MMOSPA), as applied to angular superresolution of closely-spaced targets, against Maximum a Posteriori (MAP) and Minimum Mean Squared Error (MMSE) methods. MMOSPA estimators sacrifice target labeling, but in doing so they can (often) avoid coalescence of estimates of closely-spaced objects. A compressive sensing solution, which is a form of MAP estimation, is also considered and is solved via a brute force search, which, contrary to popular belief, is computationally feasible when the number of targets is low, having execution times on the order of tens of milliseconds for two targets on a linear array.