Algorithm Development for Ultrasonic Sensing System for naval Mine Detection
Abstract
Algorithm Development for Ultrasonic Sensing System for Naval Mine Detection. Low frequency broadband sonar has been used for decades in towed arrays and more recently in Autonomous Underwater Vehicles to find Naval mines. These systems work up to the low 100’s of kHz range and can image over relatively long distances, but image resolution is limited. Higher frequency diver operated sonar systems have also been developed. These, generally operate near 2 MHz, are intended to image out to about 10 meters, allow 2D tomographic visualization of their immediate surroundings, and have significantly higher resolution. Recent wars have seen rapid IED and Counter-IED technology developments. The same thing could happen in the underwater space with Naval mines. It would be advantageous to have much higher resolution image data to identify and counter new types of mines or existing mines such as bottom mines that are more likely to evade remote sweeping. Our lab is currently developing an ultra-high frequency, 8-10 MHz, sonar system for underwater visualization, intended to image out to 1 meter with sub-millimetric resolution in order to create 3D projection images of objects. The overarching goal of the current proposal is development of software needed to automatically and efficiently enhance and analyze images obtained by our system to produce high resolution imaging of mines. We will leverage our expertise in acoustics, signal processing, image formation, image analysis, and machine learning to guide undergraduate engineering student research towards this goal. Specifically, we will use machine learning techniques to improve image acquisition and reconstruction efficiency. Software will be developed to analyze raw data being collected by the sensor to identify objects of interest. Depth maps can then be more efficiently reconstructed by limiting the computation to identified regions of interest
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 18, 2025
- Source ID
- N001742010013
Entities
People
- Jason Mitchell
Organizations
- United States Navy
- Vanderbilt University