Automated Threshold Selection for Template-Based Sonar Target Detection

Abstract

This technical report describes a constant false alarm rate (CFAR) detection algorithm along with a threshold selection algorithm derived for target detection in sonar imagery. It is designed for multi-frequency sonars and combines the advantages of template-based detection with environmental adaptation. This combination avoids the numerical estimates of a K-distribution based statistical test, while exploiting geometric features and frequency response differences between the local environment and man-made targets. The detection algorithm is based on our analysis of processed data and derived from a general statistical model. The utility of CFAR algorithms is that the selection of a detection threshold can be made independently of most image variations. However, to account for the widest variety we apply a local adaptive threshold selection mechanism and demonstrate its adaptability.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2017
Accession Number
AD1040463

Entities

People

  • Bradley C. Marchand
  • Frank J. Crosby

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Detection
  • Detectors
  • Estimators
  • False Alarms
  • Frequency
  • Frequency Bands
  • Information Science
  • Matched Filters
  • Probability
  • Seabed
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Tests
  • Target Recognition
  • Unmanned Systems

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Radar Systems Engineering.