Researchers

Kevin Williams

Executive Director

Acoustics Department

APL-UW

Associate Professor, Oceanography

Timothy Marston

Principal Engineer

Acoustics Department

APL-UW

Tim McGinnis

Sr. Principal Engineer

OE Department

APL-UW

Ben Brand

Research Scientist/Engineer - Senior

OE Department

APL-UW

Nick Michel-Hart

Head, OE Department

Principal Engineer

OE Department

APL-UW

Vern Miller

Senior Principal Engineer

OE Department

APL-UW

Mike Kenney

Principal Oceanographer

OE Department

APL-UW

Funding

ESTCP & SERDP

DOD's Environmental Research Programs

MuST

Multi-Sensor Towbody Detects, Geolocates, and Classifies UXO

A towbody and surface vessel support infrastructure to detect and classify hazards on the seafloor

Our goal is to look into the sediment. Many sonar systems can show you what's lying on the bottom. The MuST system images buried targets and tells us something about their properties, too, which is novel.

a problem

Munitions, left behind from past military training and weapons testing activities, litter shallow water environments at hundreds of sites encompassing millions of acres. The DoD is mandated to remediate these hazards and return the lands to the citizens of the U.S.

a team of physicists and engineers

an effective, expandable solution

APL-UW physicists, through a series of laboratory and field experiments, have come to understand target acoustic scattering, the effects of the seabed, how to detect and classify buried targets, and how to make classification decisions amid acoustic clutter. Mechanical and ocean engineers designed and built a modular system of subsea sensing and topside data acquisition and processing technologies. Signal processing optimization now enables onboard real-time visualization of buried UXOs, as well as buried cables, archeological artifacts, and other structures.

The MacArtney FOCUS 3 towbody and EdgeTech eBOSS sonar deployed from a support vessel are a versatile and cost-effective combination of platforms to test UXO remediation strategies. During recent surveys at field test sites seeded with targets and clutter unknown to the team, MuST demonstrated correct detection and classification of UXOs.

recent publications

Underwater unexploded ordnance (UXO) classification using a matched subspace classifier with adaptive dictionaries

Hall, J.J., M.R. Azimi-Sadjadi, S.G. Karl, Y. Zhao, and K.L. Williams, "Underwater unexploded ordnance (UXO) classification using a matched subspace classifier with adaptive dictionaries," IEEE J. Ocean. Eng., 44, 739-752, doi:10.1109/JOE.2018.2835538, 2019.

More Info

1 Jul 2019

This paper is concerned with the development of a system for the discrimination of military munitions and unexploded ordnance (UXO) in shallow underwater environments. Acoustic color features corresponding to calibrated target strength as a function of frequency and look angle are generated from the raw sonar returns for munition characterization. A matched subspace classifier (MSC) is designed to discriminate between different classes of detected contacts based upon the spectral content of the sonar backscatter. The system is exclusively trained using model-generated sonar data and then tested using the measured Target and Reverberation Experiment 2013 (TREX13) data sets collected from a synthetic aperture sonar system in a relatively low-clutter environment. A new in situ supervised learning method is also developed to incrementally train the MSC using a limited number of labeled samples drawn from the TREX13 data sets. The classification results of the MSC are presented using standard performance metrics, such as receiver operating characteristic curve and confusion matrices.

media coverage

APL-UW Undergraduate Internship

Applied research experience with MuST

Here, aboard the R/V Robertson, the students can see clearly the multidisciplinary team effort it takes to understand a problem, develop a system, and refine it for practical application in the field.

ESTCP 2020 Project of the Year

A virtual presentation by K. Williams

All team members have performed at the highest level to achieve results to date: demonstrating MuST operation at a research level, addressing navigation challenges, and advancing detection and classification signal processing.

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