Multichannel Detection and Acoustic Color-Based Classification of Underwater UXO in Sonar

Abstract

The Department of Defense (DoD) is currently responsible for clearing many sites that are potentially contaminated with munitions as a result of past training and weapons testing activities. In many cases, these activities occurred near or were performed in shallow water environments where munitions pose threats to public safety and the environment. The objective of this SERDP Exploratory Development (SEED) project is to develop efficient signal processing techniques for the detection and classification of military munitions in shallow underwater environments using data collected from synthetic aperture sonar (SAS) systems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2020
Accession Number
AD1217105

Entities

People

  • Mahmood Azimi-sadjadi
  • Steven Kargl

Organizations

  • Colorado State University
  • University of Washington

Tags

DTIC Thesaurus Topics

  • Autonomous Underwater Vehicles
  • Computational Science
  • Computer Vision
  • Detection
  • Detectors
  • Frequency Bands
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Munitions
  • Neural Networks
  • Pattern Recognition
  • Physics Laboratories
  • Signal Processing
  • Supervised Machine Learning
  • Two Dimensional
  • Unexploded Ammunition

Readers

  • Acoustical Oceanography.
  • Aviation Safety Risk Assessment.
  • Military/Explosive Ordnance Disposal (EOD) Technology