Compressive Stepped-Frequency Continuous-Wave Ground-Penetrating Radar

Suksmono, Andriyan Bayu; Bharata, E.; Lestari, A.A.; Yarovoy, Alexander; Ligthart, L.P.

doi:10.1109/lgrs.2010.2045340

Cited by 101 publications

(49 citation statements)

References 10 publications

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“…Suksmono et al [11] showed a prototype of a SFCW GPR system using a frequency synthesizer to automatically choose a different set of frequency points for each observation location. The sampled frequency information is used to reconstruct the time domain signal in each spatial position.…”

Section: Proposed Data Acquisition Methodsmentioning

confidence: 99%

“…Several other studies on CS application for SAR systems have also been conducted [7][8][9]. The implementation of CS for SFCW GPR system has been demonstrated by Gurbuz et al [10] and Suksmono et al [11]. These studies proved through simulation and experiment that CS can recover the radar image with higher resolution and less clutter than the conventional processing with much less measurement.…”

Section: Introductionmentioning

confidence: 98%

“…Previous studies ( [5,11]) showed a random data acquisition technique by reducing the number of frequency steps in each scan position, which enabled much faster scanning times. Other papers ( [6,10]) showed an alternative technique that exploited the spatial sparsity of the data by sampling not only in the frequency domain but also in the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

“…Other papers ( [6,10]) showed an alternative technique that exploited the spatial sparsity of the data by sampling not only in the frequency domain but also in the spatial domain. Unfortunately, even though the previous CS-GPR studies [10][11][12][13] has presented real experimental results, they are mostly focused only on reducing the frequency sample, and have not shown the effect of reducing spatial sampling to the performance of the CS-GPR experiment. Reducing frequency sampling would increase scanning speed, which is important in a number of applications where the target is in motion, such as ISAR.…”

Section: Introductionmentioning

confidence: 99%

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Modified Bayesian algorithm implemented in compressive sensing applied to spatially sampled GPR measurement under high clutter conditions

Karlina

Suzuki

2018

NOLTA

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This paper investigates the implementation of compressive sensing (CS) for stepped frequency continuous wave ground penetrating radar (SFCW GPR) imaging system. Previous works in this field mostly focus on reducing the frequency samples in the measurement. While this approach enables faster scanning speed, we consider reducing spatial sampling is more efficient in reducing the data acquisition time in the GPR survey over a very large area. In this study we propose a data acquisition method and CS algorithm. A two-step sampling scheme is presented. In the first step, the spatial sampling was directly conducted in the data acquisition process. In the second step, the frequency sampling was conducted offline during the signal processing. Full frequency information was used in pre-processing to suppress the noise and clutter in the experiment data. To solve the sparse-data problem, some CS algorithms are compared and a modified Bayesian approach based on fast relevance vector machine (RVM) is proposed. The performance of the proposed CS-GPR system is analyzed using the real experimental GPR data set which contains non ideal conditions, e.g. high level clutter, not truly sparse targets, and inaccurate estimation of wave velocity in the medium. Using the proposed data acquisition and CS algorithm, even with these non ideal conditions, CS can give clear and stable results with high probability detection of the target.

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Section: Proposed Data Acquisition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified Bayesian algorithm implemented in compressive sensing applied to spatially sampled GPR measurement under high clutter conditions

Karlina

Suzuki

2018

NOLTA

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“…With the further development of CS theory, considerable work has been done to apply CS to SFGPR system establishment and image reconstruction in order to reduce the data acquisition time and improve the image quality [9][10][11][12][13][14][15][16]. In practical SFGPR measurement situation, since the distance between antennas and ground is very short, the wave reflected from the ground is much stronger than that from underground targets.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing SFGPR Imaging Algorithm Based on Subspace Projection Ground Clutter Suppression

Sun¹,

Lu²,

Zhang³

2016

PIER M

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Abstract-Stepped frequency ground penetrating radar (SFGPR) has received increasing attention in the field of ground penetrating radar technology due to its superiority in the detection performance. Compressed sensing (CS) SFGPR imaging reconstruction method can not merely reduce the measured imaging data volume, but also reconstruct target image with less sidelobe. However, the imaging algorithm using CS approach will lose efficacy in strong clutter environment. To solve this problem, a CS SFGPR imaging reconstruction method combined with subspace projection clutter suppression approach is proposed in this paper. First, all frequency domain data at each measurement position are reconstructed from reduced frequency measurements via sparse reconstruction technique. Then subspace projection ground clutter suppression technique is used to suppress the strong ground clutter. Finally, the orthogonal matching pursuit (OMP) algorithm is utilized to reconstruct the underground target image. Synthetic and experimental data processing results have verified the effectiveness and accuracy of the proposed imaging method.

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