GPR Performance in the Presence of Buried Biomass: Final Report

Abstract

The effects of buried biomass (e.g. tree roots) on GPR performance were examined through a combination of theory and experiment. In Part 1 experiments performed at Eglin AFB FL are described. Wideband polarimetric GPR data were acquired at sites where extensive root structures are known to exist. After completing those measurements the site was excavated to a depth of 24 inches and the root content was exhumed intact. Soil samples were acquired at the same time. In Part 2 analyses of the root structure and GPR data are presented. An efficient numerical model for calculating root scattering was developed using the discrete dipole approximation (DDA). The DDA model was then used to simulate GPR data collected at Eglin. Additional studies found that root scattering is roughly proportional to the dielectric contrast and to the root cross-sectional area. Additional simulations suggest that roots have a very modest affect on the signatures of targets buried under them but they are a significant source of clutter particularly at frequencies above about 0.5 GHz. A statistical study based on simulated data suggests that root-related clutter will strongly affect detectability.

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Document Details

Document Type
Technical Report
Publication Date
Apr 28, 2003
Accession Number
ADA416107

Entities

People

  • Brian A. Baertlein
  • Chi Chih Chen
  • Joel T. Johnson
  • Marissa Higgins
  • Nakasit Niltawach

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Advanced Electronics
  • Sensors

DTIC Thesaurus Topics

  • Analyzers
  • Detection
  • Detectors
  • Dielectric Permittivity
  • Electromagnetic Radiation
  • False Alarms
  • Frequency
  • Frequency Bands
  • Frequency Domain
  • Frequency Response
  • Measurement
  • Scattering
  • Simulations
  • Three Dimensional
  • Two Dimensional
  • Unexploded Ammunition
  • Warning Systems

Readers

  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Image Processing and Computer Vision.
  • Regression Analysis.