SYNTHETIC APERTURE RADAR (SAR) IMAGING WITH PHASE RETRIEVAL AND UNCERTAINTY QUANTIFICATION

Abstract

The proposed research is to develop numerical algorithms based in rigorous mathematical analysis that will address some of these challenges. Specifically, these techniques will ensure that phase information is not lost in the recovery process. Further, an empirical Bayesian approach will be adopted to improve the robustness of the image recovery as well as to provide uncertainty quantification. The PIs will collaborate with AFRL/RY scientists to apply the newly developed methods for SAR downstream processing such as coherent change detection and target location.

Document Details

Document Type
DoD Grant Award
Publication Date
Apr 20, 2023
Source ID
FA95502210411

Entities

People

  • Anne Gelb

Organizations

  • Air Force Office of Scientific Research
  • Board of Trustees of Dartmouth College
  • United States Air Force

Tags

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference