Improving Robustness, Efficiency and Accuracy of Synthetic Aperture Radar (SAR) Imaging Techniques using Multi-Measurement Vectors
Abstract
The proposed research will develop numerical algorithms that effectively use multi-measurement data collections to extract actionable information from acquired sensing data. Much research has recently been devoted to sparse signal and image recovery from multiple measurement vectors (MMV). Sometimes, as in synthetic aperture radar (SAR) over a small aperture, the collected data may not vary much. In other cases, such as MIMO SAR, the data can vary significantly. The PI will focus on these applications as prototypical sensing models. The assumption in all cases is that the underlying signals or images are jointly sparse, meaning they have some features in common with sparse representations that can be recovered from the measurement vectors. Standard sparse recovery techniques can be used separately to recover each signal or image. Joint sparsity (JS) algorithms use additional constraints to exploit this measurement coupling. The L2,1 minimization, a natural analogue of the popular L1 minimization used for single measurement sparse recovery, is commonly used.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 05, 2023
- Accession Number
- AD1230278
Entities
People
- Anne Gelb
Organizations
- Board of Trustees of Dartmouth College