Use of ground penetrating radar for landmine classification based on artificial neural network

Abstract

This research is mainly aimed at developing a technique based on neural networks to classifymetal and plastic objects buried within a range of soil conditions. In addition, the validity of this technique is also presented. The explosives in land mines are generally cased in metal or plastic containers. Identification of buried metal and plastic objects using a neural network and a sensing teclmique based on an electromagnetic method are discussed in this thesis. Neural network simulation results for plastics and metal objects in the range of soil condition are also reported. Finding the appropriate frequency window (FvV) for the Ground Penetrating Radar(CPR) operation and the development of a theoretical mathematical model is also presmted. Using this model, the appropriate FW for CPR operation is derived. Furthermore the estimation of important system parameters of CPR, modulation and detection techniques, modelling of CPR, and clutter reduction techniques are also discussed in the context of this thesis.