Narrow-Band Processing and Fusion Approach for Explosive Hazard Detection in FLGPR

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 in a full-band radar image often yields a large number of false alarms. We propose a method that uses both narrow-band and full-band radar processing, coupled with a classifier that uses complex-valued Gabor filter responses as the features. We then fuse the narrow-band and full-band images into a composite confidence map and detect local maxima in this map to produce candidate alarm locations. Full-band radar images provide a high degree of image resolution, while narrow-band images provide a means to detect targets which have a unique narrow-band signature. Experimental results for our improved detection techniques are demonstrated on data sets collected at a US Army test site.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA586217

Entities

People

  • David C. Wong
  • James M. Keller
  • K. C. Ho
  • Mehrdad Soumekh
  • Timothy C Havens
  • Tuan T. Ton

Organizations

  • University of Missouri

Tags

Communities of Interest

  • Counter IED
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Change Detection
  • Detection
  • Detectors
  • False Alarms
  • Feature Selection
  • Frequency
  • Frequency Bands
  • Ground Penetrating Radar
  • Hot Spots
  • Machine Learning
  • Radar
  • Radar Images
  • Supervised Machine Learning
  • Synthetic Aperture Radar
  • Warning Systems

Readers

  • Image Processing and Computer Vision.
  • Sensor Fusion and Tracking Systems.