Accession Number:

ADA625751

Title:

New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging

Descriptive Note:

Final performance rept. 14 Jun 2014-14 Jun 2015

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE DEPT OF MATHEMATICS

Personal Author(s):

Report Date:

2015-10-26

Pagination or Media Count:

7.0

Abstract:

The PI led a collaborative effort to quantify the super-resolution potential of different computational methods for the directionfinding problem in sensing and surveillance. The difficulty of super-resolution is summarized in three quantities the super-resolution factor, the signal-to-noise ratio, and the number of targets, and tight scalings between these quantities are presented to decide whether some methods can succeed -- or every method must fail -- at the target detection task. The analysis identifies the algorithms that perform well, and those that dont, even in the case of targets that shadow each other nearby azimuths, different ranges.

Subject Categories:

  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE