Sensor Performance Analysis for Mine Detection with Unmanned Vehicles in Very Shallow Water and Surf Zones
Abstract
The very shallow water and surf zones present extraordinary challenges for classifying submerged objects such as mines or shoals. Accessing these areas with traditional unmanned underwater vehicles is difficult, and remotely operated vehicles often require putting operators in harms way. This research explores the potential to perform object classification using only forward-looking sonar in the desired operating zones. Experiments were conducted in a controlled environment for two different target objects, a glass sphere and a rectangular cinder block. Next, forward-looking sonar images were analyzed to determine how the intensity and distribution of target returns changed as a function of distance and angle from the sonar. The ability to correlate experimentally measured intensity profiles with a targets physical size and shape is examined. Finally, recommendations for future research are proposed to further develop this approach for potential naval applications like mine countermeasures.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2022
- Accession Number
- AD1184866
Entities
People
- Alexander J. Fedorovich
Organizations
- Naval Postgraduate School