Environmentally Adaptive UXO Detection and Classification Systems

Abstract

Detection, classification, and remediation of military munitions and unexploded ordnance in shallow water is of utmost importance to many DoD agencies owing to the severity of threats they pose to humans and the environment. The problem is technically challenging due to variability in environmental conditions as well as obscuration of the munitions. Thus, new methods are needed to rapidly and reliably assess large areas that are potentially contaminated with munitions and detect, localize, and identify each individual threat with a high degree of accuracy. To this end, this research addresses an important shortcoming of the existing Automatic Target Recognition (ATR) algorithms that use sonar data by developing new environmentally adaptive algorithms for the detection and classification of military munitions in shallow underwater environments using data collected from low frequency broadband SAS systems.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2016
Accession Number
AD1022583

Entities

People

  • Nick Klausner

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Climate Change
  • Data Mining
  • Data Sets
  • Department Of Defense
  • Detection
  • Detectors
  • Dimensionality Reduction
  • False Alarms
  • Feature Extraction
  • Information Operations
  • Information Retrieval
  • Information Science
  • Machine Learning
  • Random Variables
  • Target Recognition
  • Unexploded Ammunition
  • Unmanned Underwater Vehicles

Readers

  • Acoustical Oceanography.
  • Munitions and Ordnance Engineering
  • Sensor Fusion and Tracking Systems.