Measurement of Layer Thickness and Permittivity Using a New Multilayer Model From GPR Data

Kao, Chien-Ping; Li, Jing; Wang, Ying; Xing, Huichun; Liu, Ce Richard

doi:10.1109/tgrs.2007.900980

Cited by 41 publications

(10 citation statements)

References 7 publications

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“…But in SAR, the speed of the electromagnetic wave is known whereas in GPR applications, the wave speed is unknown and multiple layers could be present and be located between the ground and the buried objects. Several approaches are used to estimate the dielectric and the layer parameters of the subsoil, see for instance [15,16]. In [17], the link between an estimation error on the dielectric constant and the depth localisation is shown.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive detection of buried pipes using GPR

et al. 2017

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Detection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is intricate for three main reasons. First, noise is important in the resulting image because of the presence of several rocks and/or layers in the ground, highly influencing the Probability of False Alarm (PFA) level. Also, wave speed and object responses are unknown in the ground and depend on the relative permittivity, which is not directly measurable. Finally, the depth of the pipes leads to strong attenuation of the echoed signal, leading to poor SNR scenarios. In this paper, we propose a detection method: (1) enhancing the signal of interest while reducing the noise and layer contributions, and (2) giving a local estimate of the relative permittivity. We derive an adaptive detector where the signal of interest is parametrised by the wave speed in the ground. For this detector, noise is assumed to follow a Spherically Invariant Random Vector (SIRV) distribution in order to obtain a robust detection. We use robust maximum likelihood-type covariance matrix estimators called M-estimators. To handle the significant amount of data, we consider regularised versions of said estimators. Simulation will allow to estimate the relation PFA-Threshold. Comparison is performed with standard GPR processing methods, showing the aptitude of the method in detecting pipes having low response levels with a reasonable PFA.

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Section: Introductionmentioning

confidence: 99%

Robust adaptive detection of buried pipes using GPR

et al. 2017

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“…In this paper, we focus on layered media concerning which a lot of works have been done so far. Kao et al created a new multilayer model and used the common-middle-point method of seismic exploration to estimate the thickness and dielectric constants of highway pavements [9]. However, the echoes of reflective layers need to be distinguishable in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Determination of Parameters of Subsurface Layers Using GPR Spectral Inversion Method

Huang

Zhang

2014

IEEE Trans. Geosci. Remote Sensing

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A step-by-step spectral inversion algorithm is proposed in this paper to estimate the thickness, depth, permittivity, and conductivity of layered media from ground-penetrating radar (GPR) signals. Due to the distinct GPR spectral responses to media parameters, different attributes of the spectra are used to estimate, separately, one or several parameters within each step. Such estimated parameters are then used as starting values in a nonlinear optimization problem for the inversion of all parameters simultaneously from the whole GPR spectral data. The cost function of the optimization problem is defined as the difference between the modeled generalized reflection coefficient spectrum of subsurface layers and the actual one. The optimization problem is solved by using a modified stochastic hill-climbing (SHC) algorithm. The method is applied on synthetic data of a wedge model and real data from two highway GPR detections. The results show that the step-by-step spectral inversion method can reduce the ambiguity of the inversion and improve its accuracy and efficiency.

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“…Under the preparations like these, the unknown parameters can be estimated by a non-linear least estimation method. Here, although the specific inductive capacity of the ground is necessary for this method, it can be obtained by applying CMP(Common Mid Point) method in the fields [12].…”

Section: Introductionmentioning

confidence: 99%

A geometrical analysis of buried flat-plates on ground penetrating radar images

Nomura

Naganuma

Kato

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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We proposed a synthetic aperture GPR (Ground Penetration Radar) for buried flat-plates. The geometrical conditions of the flat-plates, i.e., the length, the inclination angles, the horizontal positions, and the buried depth are estimated by using a nonlinear least-squares method: the geometrical modelcalculated propagation times are fit to the observed ones at all the antenna positions. We carried out some survey experiments to inspect a metallic plate being buried in soil medium, and, furthermore, compared the proposed algorithm with a visual observation-based method. As a result, we confirmed the effectiveness of the proposed algorithm.

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Measurement of Layer Thickness and Permittivity Using a New Multilayer Model From GPR Data

Cited by 41 publications

References 7 publications

Robust adaptive detection of buried pipes using GPR

Robust adaptive detection of buried pipes using GPR

Determination of Parameters of Subsurface Layers Using GPR Spectral Inversion Method

A geometrical analysis of buried flat-plates on ground penetrating radar images

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