Numerical modelling and neural networks for landmine detection using ground penetrating radar

Giannakis, Iraklis, Giannopoulos, Antonios, Warren, Craig and Davidson, Nigel (2015) Numerical modelling and neural networks for landmine detection using ground penetrating radar. In: Advanced Ground Penetrating Radar (IWAGPR), 2015 8th International Workshop on, 7-10 July 2015, Florence, Italy.

Full text not available from this repository.


A numerical modelling case study is presented aiming to investigate aspects of the applicability of artificial neural networks (ANN) to the problem of landmine detection using ground penetrating radar (GPR). An essential requirement of ANN and machine learning in general, is an extensive training set. A good training set should include data from as many scenarios as possible. Therefore, a training set consisting of simulated data from a diverse range of models with varying: topography, soil inhomogeneity, landmines, false alarm targets, height of the antenna, depth of the landmines, has been produced and used. Previous approaches, have employed limited training sets and as a result they often have underestimated the capabilities of ANN. In this preliminary study, a 2D Finite-Difference Time-Domain (FDTD) model has been used as the training platform for ANN. Although a 2D approach is clearly a simplification that cannot directly translate to a practical application, it is a computationally efficient approach to examine the performance of ANN subject to an extensive training set. The results are promising and provide a good basis to further expand this approach in the future by employing even more realistic, but computationally expensive, 3D models and well-characterised, real data sets.

Item Type: Conference or Workshop Item (Paper)
Identifier: 10.1109/IWAGPR.2015.7292682
Page Range: pp. 1-4
Identifier: 10.1109/IWAGPR.2015.7292682
Subjects: Construction and engineering > Electrical and electronic engineering
Depositing User: Iraklis Giannakis
Date Deposited: 18 May 2018 09:44
Last Modified: 28 Aug 2021 07:25

Actions (login required)

View Item View Item