Machine learning methods for imaging with applications to space surveillance
Abstract
We have worked for the last five years on low earth orbit satellite imaging with ground-based radar. We focused on distributed and asynchronous ground-based illumination, which requires the use of cross correlations of the data for image formation. The use of such illumination sources has the potential to reduce the cost of the infrastructure needed for space surveillance, but it also has other advantages over conventional matched field imaging methods. One such advantage is that at higher frequencies (K-band and higher), which can provide better resolution, atmospheric inhomogeneities require mitigation. Using correlations is a first step in reducing image blurring and location uncertainty.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 06, 2024
- Source ID
- FA95502310352
Entities
People
- George Papanicolaou
Organizations
- Air Force Office of Scientific Research
- Stanford University
- United States Air Force