Model-Based Compressive Sensing Applied to Landmine Detection by GPR

Riafeni KARLINA; Motoyuki SATO

doi:10.1587/transele.E99.C.44

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Model-Based Compressive Sensing Applied to Landmine Detection by GPR

Riafeni KARLINA, Motoyuki SATO

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Summary :

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

Publication: IEICE TRANSACTIONS on Electronics Vol.E99-C No.1 pp.44-51

Publication Date: 2016/01/01

Publicized

Online ISSN: 1745-1353

DOI: 10.1587/transele.E99.C.44

Type of Manuscript: Special Section PAPER (Special Section on Recent Progress in Electromagnetic Theory and Its Application)

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Authors

Riafeni KARLINA
Tohoku University
Motoyuki SATO
Tohoku University

Keyword

compressive sensing (CS), stepped frequency continuous wave (SFCW), ground penetrating radar (GPR), Nyquist theorem, model based CS

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Riafeni KARLINA, Motoyuki SATO, "Model-Based Compressive Sensing Applied to Landmine Detection by GPR" in IEICE TRANSACTIONS on Electronics, vol. E99-C, no. 1, pp. 44-51, January 2016, doi: 10.1587/transele.E99.C.44.
Abstract: We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.E99.C.44/_p

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@ARTICLE{e99-c_1_44,
author={Riafeni KARLINA, Motoyuki SATO, },
journal={IEICE TRANSACTIONS on Electronics},
title={Model-Based Compressive Sensing Applied to Landmine Detection by GPR},
year={2016},
volume={E99-C},
number={1},
pages={44-51},
abstract={We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.},
keywords={},
doi={10.1587/transele.E99.C.44},
ISSN={1745-1353},
month={January},}

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TY - JOUR
TI - Model-Based Compressive Sensing Applied to Landmine Detection by GPR
T2 - IEICE TRANSACTIONS on Electronics
SP - 44
EP - 51
AU - Riafeni KARLINA
AU - Motoyuki SATO
PY - 2016
DO - 10.1587/transele.E99.C.44
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E99-C
IS - 1
JA - IEICE TRANSACTIONS on Electronics
Y1 - January 2016
AB - We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.
ER -