Sengodan, A. and Cockshott, W.P. (2012) The SIMCA algorithm for processing Ground Penetrating Radar data and its use in landmine detection. In: 11th International Conference on Information Science: Signal Processing and Their Applications, Montreal, Canada, 2-5 July 2012,
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Abstract
The main challenge of ground penetrating radar (GPR) based land mine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. But because the diameter of an AP mine can be as low as 2cm and many soils have very high attenuations at frequencies above 3GHz, the accurate detection of landmines is accomplished using advanced algorithms. Using image reconstruction and by carrying out the system level analysis of the issues involved with recognition of landmines allows the landmine detection problem to be solved. The SIMCA (’SIMulated Correlation Algorithm’) is a novel and accurate landmine detection tool that carries out correlation between a simulated GPR trace and a clutter1 removed original GPR trace. This correlation is performed using the MATLAB R processing environment. The authors tried using convolution and correlation. But in this paper the correlated results are presented because they produced better results. Validation of the results from the algorithm was done by an expert GPR user and 4 other general users who predict the location of landmines. These predicted results are compared with the ground truth data.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Cockshott, Dr William |
Authors: | Sengodan, A., and Cockshott, W.P. |
College/School: | College of Science and Engineering > School of Computing Science |
Research Group: | Computer Vision and Graphics Group |
Copyright Holders: | Copyright © 2012 The Authors |
Publisher Policy: | Reproduced with the permission of the authors |
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