Landmines discrimination using scattering parameters and an artificial neural network

In this paper, the performances of the neural-network approach for discrimination of the landmine depth from the ground surface and the landmine radius are carried out. A sensor for landmines detection consists of two microstrip antennas is used. One of the antennas is used as transmitting antenna a...

Full description

Saved in:
Bibliographic Details
Main Authors: Zainud-Deen, S.H., El-Hadad, E.S., Awadalla, K.H., Sharshar, H.A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Electromagnetic scattering
Finite difference methods
Landmine detection
Microstrip antennas
Receiving antennas
Scattering parameters
Shape
Time domain analysis
Transmitting antennas
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, the performances of the neural-network approach for discrimination of the landmine depth from the ground surface and the landmine radius are carried out. A sensor for landmines detection consists of two microstrip antennas is used. One of the antennas is used as transmitting antenna and the second is considered as receiving antenna. Circular cylindrical shape metallic landmine is used. The neural-network process data are obtained from the FDTD formulation of the electromagnetic scattering. The inputs in the input layer of the neural network are the magnitude of the mutual coupling between the microstrip antennas, |..S21| while the outputs are landmine depth from the ground surface and landmine radius. Good agreement with exact profile has been observed. The computation time and computer memory of the inverse problem are considerably reduced.
ISSN:1110-6980