Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines
Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to su...
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| Published in | IEEE geoscience and remote sensing letters Vol. 14; no. 8; pp. 1338 - 1342 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
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| Abstract | Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment. |
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| AbstractList | Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment. |
| Author | Deliang Xiang Yi Su Xiaoji Song Kai Zhou |
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| Snippet | Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often... |
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| SubjectTerms | Antennas Antipersonnel mines Clutter Clutter suppression Computer simulation Contamination Detection Ground penetrating radar ground-penetrating radar (GPR) Imaging techniques Information retrieval Land mines landmine detection Mathematical models Matrix decomposition Methods Mines Numerical simulations Permits Principal component analysis Principal components analysis Radar robust principal component analysis (RPCA) Simulation Soil contamination Sparse matrices System effectiveness Target recognition |
| Title | Improving RPCA-Based Clutter Suppression in GPR Detection of Antipersonnel Mines |
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