New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging
Final performance rept. 14 Jun 2014-14 Jun 2015
MASSACHUSETTS INST OF TECH CAMBRIDGE DEPT OF MATHEMATICS
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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.
- Active and Passive Radar Detection and Equipment