Robust adaptive detection of buried pipes using GPR

Abstract

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.

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

“…Using these features, the insulation layer effect can be suppressed/mitigated. To apply SVD on the acquired SAR raw data, the received signal in (10) is rearranged in matrix form to get (full details can be found in our previous work in [30]- [32]) 11) where N N is the additive white Gaussian noise (AWGN), N ∼ (0, σ 2 ω ), and…”

“…In such areas, surface inspection of the pipes is employed. Microwave NDT&E technique is one of the popular methods for pipeline surface inspection using methods such as ground penetration radar (GPR) [11], synthetic aperture radar (SAR) [12]. They are non-contact inspection methods and have since become hotspots in the pipeline industry because of its simplicity, high detection efficiency and most of them do not require an excitation source [13].…”

“…Despite the aforementioned developments in microwave non-destructive imaging method, there are few microwave SAR techniques used in layered media to appropriately compensate for the pipe curvature and high standoff distance introduced by the insulation layer. In [11], a robust adaptive detection method for buried pipes using GPR was proposed to enhance the signal of interest and reduces the layer contribution. This reduces the probability of false arm (PFA).…”

Detection and localization of underground networks by fusion of electromagnetic signal and GPR images

“…Theses detections are obtained by a treatment of the hyperbolas using the automatic detection by Curvelet 8 and the Adaptive method. 7 Our approach is particular because it is based on the use of two different approaches (figure 4) to analyze the same GPR data and the aggregation of their respective results to improve the detection of hyperbole and reduce the number of bad detection on each B-scan. A first filtering step is done on each hyperbola detected on a B-scan with the purpose of eliminating the noise or improve the quality of the detection.…”

A subspace approach for shrinkage parameter selection in undersampled configuration for Regularised Tyler Estimators

“…In this section we apply the proposed adaptive detection methodology to GPR images [19]. The problem consists in detecting a known signal p ∈ R N (response of a buried object) in an observation x ∈ R N , while having a secondary set of response free observations {x k } 1,K with K N .…”