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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Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 14; no. 8; pp. 1338 - 1342
Main Authors Song, Xiaoji, Xiang, Deliang, Zhou, Kai, Su, Yi
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2711251