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