A Realistic FDTD Numerical Modeling Framework of Ground Penetrating Radar for Landmine Detection

Iraklis Giannakis, Antonios Giannopoulos, Craig Warren

Research output: Contribution to journalArticlepeer-review

157 Citations (Scopus)
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Abstract

A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil's inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil's surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil's bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5-3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.
Original languageEnglish
Pages (from-to)37-51
Number of pages15
JournalIEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing
Volume9
Issue number1
Early online date28 Aug 2015
DOIs
Publication statusPublished - Jan 2016

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