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Register a new userClutter distributions for tomographic image standardization in ground-penetrating radar
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Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce three-dimensional distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude, and vary across different arrays, which makes direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or lognormal distributions. Based upon the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross-track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter-limited to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Finally, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development which needs to be robust across multiple different arrays.
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The three electromagnetic properties appearing in Maxwells equations are dielectric permittivity, electrical conductivity and magnetic permeability. The study of point diffractors in a homogeneous, isotropic, linear medium suggests the use of logarithms to describe the variations of electromagnetic properties in the earth. A small anomaly in electrical properties (permittivity and conductivity) responds to an incident electromagnetic field as an electric dipole, whereas a small anomaly in the magnetic property responds as a magnetic dipole. Neither property variation can be neglected without justification. Considering radiation patterns of the different diffracting points, diagnostic interpretation of electric and magnetic variations is theoretically feasible but is not an easy task using Ground Penetrating Radar. However, using an effective electromagnetic impedance and an effective electromagnetic velocity to describe a medium, the radiation patterns of a small anomaly behave completely differently with source-receiver offset. Zero-offset reflection data give a direct image of impedance variations while large-offset reflection data contain information on velocity variations.
The Austre Lovenbreen is a 4.6 km2 glacier on the Archipelago of Svalbard (79 degrees N) that has been surveyed over the last 47 years in order of monitoring in particular the glacier evolution and associated hydrological phenomena in the context of nowadays global warming. A three-week field survey over April 2010 allowed for the acquisition of a dense mesh of Ground-penetrating Radar (GPR) data with an average of 14683 points per km2 (67542 points total) on the glacier surface. The profiles were acquired using a Mala equipment with 100 MHz antennas, towed slowly enough to record on average every 0.3 m, a trace long enough to sound down to 189 m of ice. One profile was repeated with 50 MHz antenna to improve electromagnetic wave propagation depth in scattering media observed in the cirques closest to the slopes. The GPR was coupled to a GPS system to position traces. Each profile has been manually edited using standard GPR data processing including migration, to pick the reflection arrival time from the ice-bedrock interface. Snow cover was evaluated through 42 snow drilling measurements regularly spaced to cover all the glacier. These data were acquired at the time of the GPR survey and subsequently spatially interpolated using ordinary kriging. Using a snow velocity of 0.22 m/ns, the snow thickness was converted to electromagnetic wave travel-times and subtracted from the picked travel-times to the ice-bedrock interface. The resulting travel-times were converted to ice thickness using a velocity of 0.17 m/ns. The velocity uncertainty is discussed from a common mid-point profile analysis. A total of 67542 georeferenced data points with GPR-derived ice thicknesses, in addition to a glacier boundary line derived from satellite images taken during summer, were interpolated over the entire glacier surface using kriging with a 10 m grid size. Some uncertainty analysis were carried on and we calculated an averaged ice thickness of 76 m and a maximum depth of 164 m with a relative error of 11.9%. The volume of the glacier is derived as 0.3487$pm$0.041 km3. Finally a 10-m grid map of the bedrock topography was derived by subtracting the ice thicknesses from a dual-frequency GPS-derived digital elevation model of the surface. These two datasets are the first step for modelling thermal evolution of the glacier and its bedrock, as well as the main hydrological network.
We address the problem of robot localization using ground penetrating radar (GPR) sensors. Current approaches for localization with GPR sensors require a priori maps of the systems environment as well as access to approximate global positioning (GPS) during operation. In this paper, we propose a novel, real-time GPR-based localization system for unknown and GPS-denied environments. We model the localization problem as an inference over a factor graph. Our approach combines 1D single-channel GPR measurements to form 2D image submaps. To use these GPR images in the graph, we need sensor models that can map noisy, high-dimensional image measurements into the state space. These are challenging to obtain a priori since image generation has a complex dependency on subsurface composition and radar physics, which itself varies with sensors and variations in subsurface electromagnetic properties. Our key idea is to instead learn relative sensor models directly from GPR data that map non-sequential GPR image pairs to relative robot motion. These models are incorporated as factors within the factor graph with relative motion predictions correcting for accumulated drift in the position estimates. We demonstrate our approach over datasets collected across multiple locations using a custom designed experimental rig. We show reliable, real-time localization using only GPR and odometry measurements for varying trajectories in three distinct GPS-denied environments. For our supplementary video, see https://youtu.be/HXXgdTJzqyw.
Traveltime tomography is a very effective tool to reconstruct acoustic, seismic or electromagnetic wave speed distribution. To infer the velocity image of the medium from the measurements of first arrivals is a typical example of ill-posed problem. In the framework of Tikhonov regularization theory, in order to replace an ill-posed problem by a well-posed one and to get a unique and stable solution, a stabilizing functional (stabilizer) has to be introduced. The stabilizer selects the desired solution from a class of solutions with a specific physical and/or geometrical property; e.g., the existence of sharp boundaries separating media with different petrophysical parameters. Usually stabilizers based on maximum smoothness criteria are used during the inversion process; in these cases the solutions provide smooth images which, in many situations, do not describe the examined objects properly. Recently a new algorithm of direct minimization of the Tikhonov parametric functional with minimum support stabilizer has been introduced; it produces clear and focused images of targets with sharp boundaries. In this research we apply this new technique to real radar tomographic data and we compare the obtained result with the solution generated by the more traditional minimum norm stabilizer.
There has been exciting recent progress in using radar as a sensor for robot navigation due to its increased robustness to varying environmental conditions. However, within these different radar perception systems, ground penetrating radar (GPR) remains under-explored. By measuring structures beneath the ground, GPR can provide stable features that are less variant to ambient weather, scene, and lighting changes, making it a compelling choice for long-term spatio-temporal mapping. In this work, we present the CMU-GPR dataset--an open-source ground penetrating radar dataset for research in subsurface-aided perception for robot navigation. In total, the dataset contains 15 distinct trajectory sequences in 3 GPS-denied, indoor environments. Measurements from a GPR, wheel encoder, RGB camera, and inertial measurement unit were collected with ground truth positions from a robotic total station. In addition to the dataset, we also provide utility code to convert raw GPR data into processed images. This paper describes our recording platform, the data format, utility scripts, and proposed methods for using this data.
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