Subspace Clutter Removal Techniques in GPR Images

Shehab, Mohanad Abd; Obaidi, Mohammed Abdulridha Sahib Al; Hoş, I.; Karamzadeh, and Saeid

doi:10.2528/pierm19032511

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Germane cognitive load refers to instructional manipulations that assist the learner to use working memory resources to learn a specific activity or task. Shehab (2011) confirmed that long-term memory is the main knowledge storage for new knowledge and skills, whereas working memory underlies the processing of new information. Grobe (2018) stated that information store principle is a huge long-term memory capacity to store organized information in the form of schemas that can be handled as a single element in working memory.…”

Section: Sweller Et Almentioning

confidence: 68%

Cognitive Load of Assigned EFL Textbooks for Al-Azhar Secondary School Students: An Analytical Study.

Yussuf¹,

Ismail

Atta

2022

Sohag University International Journal of Educational Research

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Section: Sweller Et Almentioning

confidence: 68%

Cognitive Load of Assigned EFL Textbooks for Al-Azhar Secondary School Students: An Analytical Study.

Yussuf¹,

Ismail

Atta

2022

Sohag University International Journal of Educational Research

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“…Two of the benchmark cases include different data sets generated using PCA 26 , 47 , 48 . The first one consists of features extracted using PCA similar as in study 26 for the purpose of dimensionality reduction of the B-scan.…”

Section: Methodsmentioning

confidence: 99%

“…As a result, the input vector is prepared to have the size of 1 × 874 for the proposed M2LP framework. The second benchmark case is specialized on clutter reduction by using PCA 47 , 48 , where only the reflections coming from the buried object are obtained and linear regression technique is employed to identify hyperbolic signatures of the B-scan data. After that, the proposed deep-learning-based approach, M2LP framework is used to carry out buried object characterization.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 12 provides examples of specific scenarios and corresponding object parameter prediction for the proposed approach and the benchmark methods. Two of the benchmark cases include different data sets generated using PCA 26,47,48 . The first one consists of features extracted using PCA similar as in study 26 for the purpose of dimensionality reduction of the B-scan.…”

Section: Design Of Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Yurt

Torpi

Kızılay

et al. 2023

Sci Rep

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This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently of each other. The proposed methodology capitalizes on a fast and accurate data-driven surrogate model developed for object characterization in terms of its vertical and lateral position, and the size. The surrogate is constructed in a computationally efficient manner as compared to methodologies using 2D B-scan image. This is achieved by operating at the level of hyperbolic signatures extracted from the B-scan data through linear regression, which effectively reduces the dimensionality and the size of data. The proposed methodology relies on reducing of 2D B-scan image to 1D data including variation of reflected electric fields’ amplitudes with respect to the scanning aperture. The input of the surrogate model is the extracted hyperbolic signature obtained through linear regression executed on the background subtracted B-scan profiles. The hyperbolic signatures encode information about the geophysical parameters of the buried object, including depth, lateral position, and radius, all of which can be extracted using proposed methodology. Parametric estimation of the object radius and the estimation of the location parameters simultaneously is a challenging problem. Applying the application of processing steps on B-scan profiles incurs high computational costs, which is a limitation of the current methodologies. The metamodel itself is rendered using a novel deep-learning-based modified multilayer perceptron (M2LP) framework. The presented object characterization technique is favourably benchmarked against the state-of-the-art regression techniques, including Multilayer Perceptron (MLP), Support Vector Regression Machine (SVRM), and Convolutional Neural Network (CNN). The verification results demonstrate the average mean absolute error of 10 mm, and the average relative error of 8 percent, both corroborating the relevance of the proposed M2LP framework. In addition, the presented methodology provides a well-structured relation between the geophysical parameters of object and the extracted hyperbolic signatures. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. The environmental and internal noise of the GPR system and their effect is analyzed as well. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

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“…The most prominent eigenvalues result from environmental clutter and only a minor part is due to system noise. When only two subspaces are used (noise and data), the clutter is part of the data matrix and cannot be removed (Sabushimike et al, 2016;Shehab et al, 2019). Considering additive noise, a measured frame of a radar system A meas is an M × N matrix, where N is the number of chirps and M is the number of time-steps.…”

Section: Data and Noise Subspacesmentioning

confidence: 99%

Dynamic Eigenimage Based Background and Clutter Suppression for Ultra Short-Range Radar

et al. 2021

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Abstract. Background and clutter suppression techniques are important towards the successful application of radar in complex environments. We investigate eigenimage based methodologies such as principal component analysis (PCA) and apply it to frequency modulated continuous wave (FMCW) radar. The designed dynamic principal component analysis (dPCA) algorithm dynamically adjusts the number of eigenimages that are utilised for the processing of the signal. Furthermore, the algorithm adapts towards the number of objects in the field of view as well as the estimated distances. For the experimental evaluation, the dPCA algorithm is implemented in a multi-static FMCW radar prototype that operates in the K-band at 24 GHz. With this background and clutter removal method, it is possible to increase the signal-to-clutter-ratio (SCR) by 4.9 dB compared to standard PCA with mean removal (MR).

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Subspace Clutter Removal Techniques in GPR Images

Cited by 9 publications

References 12 publications

Cognitive Load of Assigned EFL Textbooks for Al-Azhar Secondary School Students: An Analytical Study.

Cognitive Load of Assigned EFL Textbooks for Al-Azhar Secondary School Students: An Analytical Study.

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Dynamic Eigenimage Based Background and Clutter Suppression for Ultra Short-Range Radar

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