Identification of buried antipersonnel landmines with ground penetrating radar (GPR) establishes a need for scattering models relating the measured scattered field to target characteristics such as size, material composition and burial depth. In this paper, the authors present generalizations of their previously published convolutional models for plane wave back-scattering from a dielectric minelike target embedded in an unbounded host medium, which account for the ground surface, the GPR hardware and internal mine structure. Using 3D finite-difference time-domain (FDTD) and measured data examples, the authors illustrate the validity of the convolutional models and how they can be used to characterize buried targets. In particular, they show that it is possible to determine target size and depth with millimeter accuracy under laboratory conditions, both of which are valuable information for landmine identification.
Radar scattering models for the identification of buried low-metal content landmines
2005
4 Seiten, 7 Bilder, 6 Quellen
Conference paper
English
Antennencharakteristik , dielektrischer Stoff , dreidimensionale Darstellung , FDTD-Analyse , Halbraum , Impulsantwort , Messgenauigkeit , Messung geometrischer Größen , Metallsuchgerät , Mine (Waffe) , Modellsimulation , Objekterkennung , Radarantenne , Radaranwendung , streuendes Objekt , Streufaktor , Streuung , Vergraben
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