- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil, Switzerland (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - Noise suppression of GPR data using Variational Mode Decomposition
摘要： Ground penetrating radar (GPR) has been used in the many aspects, such as civil engineering and the earth sciences. And the analysis and noise suppression of GPR data have always been the research focus. In this study, a new self-adaptive time-frequency decomposition tool called the variational mode decomposition (VMD) is introduced. We use the VMD method to derive a set of stationary sub-components, and based on the decomposition, we separate the valid signals and the components which are corresponded to the noise. One trace of GPR data are given to test the effect of the VMD decomposition, and the empirical mode decomposition (EMD) is also employed as a comparison. And a primary noise-suppression method based on the VMD scheme is also proposed. The application of the field GPR data further demonstrates the better performance of the proposed method in both noise suppression and the retention of geophysical events.
关键词： ground penetrating radar (GPR),mode decomposition,variational mode decomposition (VMD),noise reduction or suppression
更新于2025-09-23 15:23:52
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Sensitive Damage Detection of Reinforced Concrete Bridge Slab by ``Time-Variant Deconvolution'' of SHF-Band Radar Signal
摘要： In this paper, we focus on ground-penetrating radar (GPR) for infrastructural health monitoring, especially for the monitoring of reinforced concrete (RC) bridge slab. Due to the demand of noncontact and high-speed monitoring technique which can handle vast amounts of aging infrastructures, GPR is a promising tool. However, because radar images consist of many reflected waves, they are usually difficult to interpret. Furthermore, the spatial resolution of system is not enough considering the thickness of target damages, cracks, and segregation are millimeter-to-centimeter order while the wavelength of ordinary GPR ultrahigh-frequency band is over 10 cm. To address these problems, for the purpose of sensitive damage detection, we propose a new algorithm based on deconvolution utilizing a super high-frequency (SHF) band system. First, a distribution of reflection coefficient is inversely estimated by 1-D bridge slab model. Because concrete is found to be a lossy medium at SHF band, we consider the attenuation of signal in deconvolution. The algorithm is called 'time-variant deconvolution' in this paper. After the validation by simulation, the effects of the algorithm and frequency band on damage detection accuracy are evaluated by a field experiment. Though the results show a 1-mm horizontal crack is not detected by measured waves, when it is filled with water, it is detected by time-variant deconvolution. Moreover, the 1-mm dried crack is detected only by time-variant deconvolution at SHF band, which greatly emphasizes the peaks of the reflection coefficient of the crack.
关键词： thin cracks and segregation detection,Ground-penetrating radar (GPR),infrastructural health monitoring,time-variant deconvolution
更新于2025-09-23 15:23:52
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[IEEE 2018 17th International Conference on Ground Penetrating Radar (GPR) - Rapperswil (2018.6.18-2018.6.21)] 2018 17th International Conference on Ground Penetrating Radar (GPR) - Groundwater table level changes based on ground penetrating radar images: a case study
摘要： A ground penetrating radar has been used to estimate the depth of groundwater table. The GPR measurements were conducted on esker deposits along the same profile and repeated five times during the year in autumn, spring, two times in summer and again in autumn. A shielded transmitting antenna with a nominal frequency of 250 MHz was used during the surveys. The accuracy of ground penetrating radar measurements to estimate the depth of groundwater occurrence is discussed in this paper. The results of estimation of groundwater table from GPR is compared with the level of groundwater table measured in piezometer.
关键词： ground penetrating radar (GPR),groundwater table,monitoring
更新于2025-09-23 15:22:29
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B-scan wave outline analysis in numerical modeling of ground-penetrating radar response from layered rough interfaces
摘要： Imaging of rough interfaces in a layered structure requires full understanding of the characteristics of their ground penetrating radar (GPR) echoes. In this study, a finite-difference time-domain computational model using a uniaxial perfectly matched layer boundary for GPR demining of layered rough interfaces is constructed. On the basis of this model, the numerical results of B-scan echoes from two-layered and three-layered rough interfaces with different degrees of roughness are obtained and compared with the profiles of corresponding rough surfaces. These results and comparisons highlight the relationship between the B-scan wave outlines and the profile of the layered rough interfaces. The effect of roughness of the interface on the B-scan echoes are analyzed, and the influence of the upper rough surface profile on the shape of the B-scan wave outline from the lower rough surface is discussed.
关键词： layered rough interfaces demining,finite-difference time-domain method (FDTD),ground penetrating radar (GPR),echo characteristic analysis
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Atlanta, GA, USA (2019.7.7-2019.7.12)] 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Propagation Characteristics of a Reconfigurable Plasmonic Rectangular Groove Grating Waveguide Using Periodically Photoinduced Plasma
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth's surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： variational Bayes (VB),hidden landmine detection,Ground penetrating radar (GPR),multiple-instance learning (MIL)
更新于2025-09-23 15:21:01
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A Modified Min-Norm for Time Delay and Interface Roughness Estimation by Ground Penetrating Radar: Experimental Results
摘要： The development of methods and tools for the road infrastructure sustainable management is a research challenge, especially for nondestructive testing methods. This letter focuses on the estimation of the thickness of civil engineering structures, like pavements, and more precisely, the time delay and interface roughness. We propose a modified Min-Norm algorithm which allows efficiently estimating the time delay and interface roughness without the eigenvalue decomposition. Therefore, it has a smaller computational load compared with subspace-based methods. The experimental results show the efficiency of the proposed algorithm.
关键词： time delay estimation,interface roughness,pavement survey,Ground penetrating radar (GPR)
更新于2025-09-23 15:21:01
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Lightweight Broadband Antennas for UAV based GPR Sensors
摘要： In this contribution two different types of broadband antennas namely a logarithmic-periodic dipole antenna (LPDA) and a transversal electromagnetic (TEM) horn antenna for ground penetrating synthetic aperture radar sensors are presented. These antennas are designed to be mounted on an unmanned aircraft vehicle (UAV). The antennas are evaluated in terms of their matching, radiation pattern pulse response and weight. Finally, an integration example of one of these antennas is shown on an UAV.
关键词： ultra wideband antennas,dispersion,unmanned aerial vehicles,Ground penetrating radar
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Beijing, China (2019.11.21-2019.11.23)] 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Demand Response Quantification Strategy for High Photovoltaic Penetration Systems with Thermal Ramp Rate Limitation
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： variational Bayes (VB),hidden landmine detection,Ground penetrating radar (GPR),multiple-instance learning (MIL)
更新于2025-09-19 17:13:59
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[IEEE 2019 International Energy and Sustainability Conference (IESC) - Farmingdale, NY, USA (2019.10.17-2019.10.18)] 2019 International Energy and Sustainability Conference (IESC) - Influence of Photovoltaic Installations on Employability and Education in Senegal
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： variational Bayes (VB),hidden landmine detection,Ground penetrating radar (GPR),multiple-instance learning (MIL)
更新于2025-09-19 17:13:59
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A plasmonic nano-biosensor based on two consecutive disk resonators and unidirectional reflectionless propagation effect
摘要： Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth’s surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.
关键词： multiple-instance learning (MIL),variational Bayes (VB),Ground penetrating radar (GPR),hidden landmine detection
更新于2025-09-19 17:13:59