Analysis of time interpolation for enhanced resolution GPR data

Strange, Andrew

doi:10.1109/iwagpr.2013.6601537

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Finally, [59] discusses the precision attainable with GPR systems for quantitative subsurface target depth measurements, which is limited to the range uncertainty associated with the time sampling interval. Nevertheless, none of these papers provides any discussion about the precision of the SNR test method, especially in terms of bias.…”

Section: Signal Processing Methodology For Astm Standardmentioning

confidence: 99%

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Benedetto

Tosti

2017

Signal Processing

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Ground penetrating radar (GPR) is one of the most promising and effective non-destructive testing techniques (NDTs), particularly for the interpretation of the soil properties. Within the framework of international Agencies dealing with the standardization of NDTs, the American Society for Testing and Materials (ASTM) has published several standard test methods related to GPR, none of which is focused on a detailed analysis of the system performance, particularly in terms of precision and bias of the testing variable under consideration. This work proposes a GPR signal processing methodology, calibrated and validated on the basis of a consistent amount of data collected by means of laboratory-scale tests, to assess the performance of the above standard test methods for GPR systems. The (theoretical) expressions of the bias and variance of the estimation error are here investigated by a reduced Taylor's expansion up to the second order. Therefore, a closed form expression for theoretically tuning the optimal threshold according to a fixed target value of the GPR signal stability is proposed. Finally, the study is extended to GPR systems with different antenna frequencies to analyze the specific relationship between the frequency of investigation, the optimal thresholds, and the signal stability

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Section: Signal Processing Methodology For Astm Standardmentioning

confidence: 99%

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

Benedetto

Tosti

2017

Signal Processing

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“…In GPR, these issues are usually solved using spline interpolation [3]. Some works explore the possibility of using interpolation to increase the resolution of the acquired GPR signals, a problem that is still under research (e.g., [4]). This improvement could help with the development of techniques that benefit from high data resolution [5].…”

Section: Introductionmentioning

confidence: 99%

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

et al. 2014

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Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans.

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“…In GPR, these issues are usually solved using spline interpolation [69]. Some works explore the possibility of using interpolation to increase the resolution of the acquired GPR signals, a problem that is still under research [252]. This improvement could help with the development of techniques that benefit from high data resolution [149].…”

Section: Ground-penetrating Radarmentioning

confidence: 99%

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Armero¹

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This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals.The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification.The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification.Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems wi...

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Analysis of time interpolation for enhanced resolution GPR data

Cited by 6 publications

References 9 publications

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

A signal processing methodology for assessing the performance of ASTM standard test methods for GPR systems

On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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