GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

Song, Xiaoji; Liu, Tao; Xiang, Deliang; Su, Yi

doi:10.3390/rs11080984

Cited by 9 publications

(3 citation statements)

References 40 publications

(75 reference statements)

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“…4) Anti-Tank vs Anti-Personnel Objects: It's not uncommon for GPR detection systems in EHD to be applied or evaluated against anti-tank (AT) or anti-personnel (AP) objects exclusively [32], [33], [20], [27], [34], [35], [36]. Specifically, in [37], the general notion that AP objects are more difficult to detect is clearly shown, as AP mines present more difficultly due to smaller size and less metallic content [1].…”

Section: A Comparison Of Model Parametersmentioning

confidence: 99%

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Stevenson,

Wilson,

Worthmann

et al. 2021

Preprint

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Comparing the observed brightness of various buried objects is a straightforward way to characterize the performance of a ground penetrating radar (GPR) system. However, a limitation arises. A simple comparison of buried object brightness values does not disentangle the effects of the GPR system itself from the system's operating environment and the objects being observed. Therefore, with brightness values exhibiting an unknown synthesis of systemic, environmental, and object factors, GPR system analysis becomes a convoluted affair. In this work, we use an experimentally collected dataset of over 25,000 object observations from five different multi-static radar arrays to develop models of buried object brightness and control for these various effects. Our modeling efforts provide a means for quantifying the relative brightness of GPR systems, the objects they detect, and the physical properties of those objects which influence observed brightness. To evaluate the models' performance on new object observations, we repeatedly simulate fitting them to half the dataset and predicting the observed brightness values of the unseen half. Additionally, we introduce a method for estimating the probability that individual observations constitute a visible object, which aids in failure analysis, performance characterization, and dataset cleaning.

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Section: A Comparison Of Model Parametersmentioning

confidence: 99%

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Stevenson,

Wilson,

Worthmann

et al. 2021

Preprint

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show abstract

“…Several attempts can be made to improve the accuracy of the RTM with the complex geologies, especially with the irregular interface. From the perspective of GPR data processing, Song and Xiang [46], [47] proposed a new clutter suppression method, which can ensure the accuracy of anti-personnel mine detection and reduce the false alarm rate of detection in a strong clutter environment. In another aspect, J. H. Bradford [48] used terrain correction to improve RTM imaging results when faced with the problem of complex terrain.…”

Section: Introductionmentioning

confidence: 99%

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

Wang

Feng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this paper, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.Index Terms-Spectral element method (SEM), ground penetrating radar (GPR), complex frequency shifted perfectly matched layer (CFS-PML), per-element GPU parallel framework.

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“…It can be seen that although these methods can suppress noise to a certain extent, they all have some shortcomings. In terms of removing clutter, principal component analysis (PCA) [28], independent component analysis (ICA) [29], morphological component analysis (MCA) [30], non-negative matrix factorization (NMF) [31], go decomposition (GoDec) [32], robust matrix factorization (RMF) [33], robust orthonormal subspace learning (ROSL) [34], robust PCA (RPCA) [35], [36], and tensor RPCA (TRPCA) [37], [38] are all good methods. They divide GPR data into two categories through different methods, one corresponding to the clutter and the other corresponding to the target signal, thus achieving the purpose of clutter removal.…”

Section: Introductionmentioning

confidence: 99%

The Noise Attenuation and Stochastic Clutter Removal of Ground Penetrating Radar Based on the K-SVD Dictionary Learning

et al. 2021

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The ground penetrating radar (GPR) data in the complex detection environment is nonstationary, non-Gaussian, and non-uniform, so the traditional noise attenuation methods are difficult to meet the requirements of denoising. Therefore, we introduced the K-singular value decomposition (K-SVD) dictionary learning into the denoising of GPR signals. It uses the orthogonal matching pursuit (OMP) algorithm to sparse decompose different radar data and trains the overcomplete dictionary with sample characteristics. K-SVD makes full use of the prior information and can extract features according to the sample data adaptively, which means it has strong sparse representation competence. Because radar signals can be sparsely represented in the dictionary, whereas the random noise does not have a sparse representation, the K-SVD dictionary can be used to distinguish effective signals from noise in the GPR data. We used the discrete cosine transform (DCT) and K-SVD dictionaries to process the Gaussian noise and stochastic clutter in the GPR profile. The results show that both K-SVD and DCT can effectively suppress the Gaussian noise. But for the clutter generated by the random medium, the DCT dictionary also causes damage to the effective signals while removing the noise; whereas the K-SVD dictionary learning algorithm uses the DCT dictionary as the initial dictionary and carries out adaptive learning on the noisy data, considering the information in the block and the global observation to complete the denoising, with good denoising effect and high fidelity. We finally verified the effectiveness and practicability of the K-SVD method for measured data.

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GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

Cited by 9 publications

References 40 publications

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

The Noise Attenuation and Stochastic Clutter Removal of Ground Penetrating Radar Based on the K-SVD Dictionary Learning

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