CS versus MAP and MMOSPA for Multi-Target Radar AOAs

We expand upon existing the literature regardingusing Minimum Mean Optimal Sub-Pattern Assignment error(MMOSPA) estimates in multitarget tracking to apply it to angularsuperresolution of closely-space targets, noting its advantagesin comparison to Maximum a Posteriori (MAP) and MinimumMean Squared Error (MMSE) estimation. MMOSPA estimatorssacrifice target labeling, but in doing so they can (often) avoidcoalescence of estimates of closely-spaced objects. A compressivesensing solution, which is a form of MAP estimation, is alsoconsidered and is solved via a brute force search, which, contraryto popular belief, is computationally feasible when the numberof targets is low, having execution times on the order of tens ofmilliseconds for two targets on a linear array

CS versus MAP and MMOSPA for Multi-Target Radar AOAs

We expand upon existing the literature regardingusing Minimum Mean Optimal Sub-Pattern Assignment error(MMOSPA) estimates in multitarget tracking to apply it to angularsuperresolution of closely-space targets, noting its advantagesin comparison to Maximum a Posteriori (MAP) and MinimumMean Squared Error (MMSE) estimation. MMOSPA estimatorssacrifice target labeling, but in doing so they can (often) avoidcoalescence of estimates of closely-spaced objects. A compressivesensing solution, which is a form of MAP estimation, is alsoconsidered and is solved via a brute force search, which, contraryto popular belief, is computationally feasible when the numberof targets is low, having execution times on the order of tens ofmilliseconds for two targets on a linear array