CS versus MAP and MMOSPA for Multi-Target Radar AOAs

Paper i proceeding, 2011

We expand upon existing the literature regarding using Minimum Mean Optimal Sub-Pattern Assignment error (MMOSPA) estimates in multitarget tracking to apply it to angular superresolution of closely-space targets, noting its advantages in comparison to Maximum a Posteriori (MAP) and Minimum Mean Squared Error (MMSE) estimation. 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.