CS versus MAP and MMOSPA for Multi-Target Radar AOAs
Paper in 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.

Author

David Crouse

P. Willett

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Y. Bar-Shalom

Proc. 45th Asilomar Conference on Signals, Systems and Computers

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Probability Theory and Statistics

Other Electrical Engineering, Electronic Engineering, Information Engineering

More information

Created

10/7/2017