Locally Adaptive Detection Algorithm for Forward-Looking Ground-Penetrating Radar

Abstract

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar (FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection often yields a large number of false alarms. We propose a locally-adaptive detection method that adjusts the detection criteria automatically and dynamically across different spatial regions, which improves the detection of weak scattering targets. The paper also examines a spectrum based classifier. This classifier rejects false alarms (FAs) by classifying each alarm location based on its spatial frequency-spectrum. Experimental results for the improved detection techniques are demonstrated by field data measurements from a US Army test site.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 22, 2011
Accession Number
ADA545174

Entities

People

  • David C. Wong
  • Dominic K. Ho
  • James M. Keller
  • Justin Farrell
  • Mihail Popescu
  • Tim C. Havens
  • Tuan T. Ton

Tags

Communities of Interest

  • Counter IED
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • False Alarms
  • Feature Selection
  • Frequency
  • Ground Penetrating Radar
  • Machine Learning
  • Pattern Recognition
  • Radar
  • Standards
  • Synthetic Aperture Radar
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Radar Systems Engineering.