Realistic simulation of GPR for landmine and IED detection including antenna models, soil dispersion and heterogeneity

Abstract

AbstractGround‐penetrating radar is an effective tool for detecting landmines and improvised explosive devices, but its performance is strongly influenced by subsurface properties as well as the characteristics of the target. To complement or replace labour‐intensive experiments on test sites, cost‐efficient electromagnetic wave propagation simulations using the finite‐difference time‐domain method are being increasingly used. However, to obtain realistic synthetic data, accurate modelling of signal alteration caused by dispersion, scattering from soil material, target contrast, shape, and inner setup, as well as the coupling effects of the antenna to the ground is required. In this study, we present a detailed three‐dimensional model of a shielded ground‐penetrating radar antenna applied to various scenarios containing metallic and non‐metallic targets buried in different soils. The frequency‐dependent intrinsic material properties of soil samples were measured with the coaxial transmission‐line technique, while a discrete random media was used to implement the heterogeneity of a gravel based on its grain‐size distribution. Our simulations show very good agreement with experimental validation data collected under controlled conditions. We accurately reproduce the amplitude, frequency, and phase of target signals, the subsurface background noise, antenna crosstalk and associated interference with target signals, and the effect of antenna elevation. The approach allows for a systematic investigation of the effects of soil, target, and sensor properties on detection performance, providing insight into novel and complex ground‐penetrating radar scenarios and the potential for a wide range of simulation possibilities for demining with ground‐penetrating radar. These investigations have the potential to improve the safety and effectiveness of landmine and improvised explosive device detection in the future, such as building a database for training deminers or developing automatic signal pattern recognition algorithms.