GPR-Based Landmine Detection and Identification Using Multiple Features
This paper presents a method to identify landmines in various burial conditions. A ground penetration radar is used to generate data set, which is then processed to reduce the ground effect and noise to obtain landmine signals. Principal components and Fourier coefficients of the landmine signals ar...
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doaj-c094b4c133674dd2b03b42c8afbce8582020-11-24T21:01:40ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58691687-58772012-01-01201210.1155/2012/826404826404GPR-Based Landmine Detection and Identification Using Multiple FeaturesKwang Hee Ko0Gyubin Jang1Kyungmi Park2Kangwook Kim3The School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of KoreaThe School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of KoreaThe School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of KoreaThe School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of KoreaThis paper presents a method to identify landmines in various burial conditions. A ground penetration radar is used to generate data set, which is then processed to reduce the ground effect and noise to obtain landmine signals. Principal components and Fourier coefficients of the landmine signals are computed, which are used as features of each landmine for detection and identification. A database is constructed based on the features of various types of landmines and the ground conditions, including the different levels of moisture and types of ground and the burial depths of the landmines. Detection and identification is performed by searching for features in the database. For a robust decision, the counting method and the Mahalanobis distance-based likelihood ratio test method are employed. Four landmines, different in size and material, are considered as examples that demonstrate the efficiency of the proposed method for detecting and identifying landmines.http://dx.doi.org/10.1155/2012/826404 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kwang Hee Ko Gyubin Jang Kyungmi Park Kangwook Kim |
spellingShingle |
Kwang Hee Ko Gyubin Jang Kyungmi Park Kangwook Kim GPR-Based Landmine Detection and Identification Using Multiple Features International Journal of Antennas and Propagation |
author_facet |
Kwang Hee Ko Gyubin Jang Kyungmi Park Kangwook Kim |
author_sort |
Kwang Hee Ko |
title |
GPR-Based Landmine Detection and Identification Using Multiple Features |
title_short |
GPR-Based Landmine Detection and Identification Using Multiple Features |
title_full |
GPR-Based Landmine Detection and Identification Using Multiple Features |
title_fullStr |
GPR-Based Landmine Detection and Identification Using Multiple Features |
title_full_unstemmed |
GPR-Based Landmine Detection and Identification Using Multiple Features |
title_sort |
gpr-based landmine detection and identification using multiple features |
publisher |
Hindawi Limited |
series |
International Journal of Antennas and Propagation |
issn |
1687-5869 1687-5877 |
publishDate |
2012-01-01 |
description |
This paper presents a method to identify landmines in various burial conditions. A ground penetration radar is used to generate data set, which is then processed to reduce the ground effect and noise to obtain landmine signals. Principal components and Fourier coefficients of the landmine signals are computed, which are used as features of each landmine for detection and identification. A database is constructed based on the features of various types of landmines and the ground conditions, including the different levels of moisture and types of ground and the burial depths of the landmines. Detection and identification is performed by searching for features in the database. For a robust decision, the counting method and the Mahalanobis distance-based likelihood ratio test method are employed. Four landmines, different in size and material, are considered as examples that demonstrate the efficiency of the proposed method for detecting and identifying landmines. |
url |
http://dx.doi.org/10.1155/2012/826404 |
work_keys_str_mv |
AT kwangheeko gprbasedlandminedetectionandidentificationusingmultiplefeatures AT gyubinjang gprbasedlandminedetectionandidentificationusingmultiplefeatures AT kyungmipark gprbasedlandminedetectionandidentificationusingmultiplefeatures AT kangwookkim gprbasedlandminedetectionandidentificationusingmultiplefeatures |
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1716777264595599360 |