Evaluation and Improvement of Spectral Features for the Detection of Buried Explosive Hazards Using Forward-Looking Ground-Penetrating Radar

Abstract

We provide an evaluation of spectral features extracted from the signal return of a forward-looking ground penetrating radar to improve the detection performance of buried explosive hazards. The evaluations are performed on data collected at two different lanes at a government test site. The performance of the one-dimensional (1D), two-dimensional (2D) and multiple (ML) spectral features will be contrasted through lane-based cross-validation for training and testing. Additional features to characterize the spectral behaviors of the forward-looking radar return will also be examined.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2012
Accession Number
ADA582038

Entities

People

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

Organizations

  • University of Missouri

Tags

Communities of Interest

  • Counter IED
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Detection
  • Detectors
  • Electrical Engineering
  • False Alarms
  • Feature Extraction
  • Frequency
  • Frequency Domain
  • Ground Penetrating Radar
  • Machine Learning
  • Radar
  • Signal Processing
  • Spectra
  • Standards
  • Synthetic Aperture Radar
  • Two Dimensional
  • Warning Systems

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.
  • Systems Analysis and Design

Technology Areas

  • AI & ML