Joint Beamforming and Automated Target Recognition for 3D SAS
Abstract
Unexploded ordnance in cluttered, occluded, and buried environments present a major environmental and security risk for munitions response. Synthetic aperture sonar technology presents an opportunity to mitigate these risks but suffer from reduced fidelity and lowered recognition for potential targets in these challenging environmental conditions. This project develops a suite of algorithms to enhance synthetic aperture sonar beamforming for automated target recognition for buried unexploded ordnance detection. Two innovations are introduced: (1) a new differentiable beamforming method for volumetric synthetic aperture sonar reconstruction, and (2) a machine learning based tone mapper for improving automated target recognition. Both methods were developed using state-of-the-art advances in machine learning and computational imaging and were evaluated on exemplar data from a sub-bottom synthetic aperture sonar of various man-made objects distributed on a lakebed. Benefits to the DoD and the scientific community include new knowledge of how to merge the physics of underwater acoustics and beamforming with machine learning.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2023
- Accession Number
- AD1217109
Entities
People
- Suren Jayasuriya
Organizations
- Arizona State University