Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets.
Ground penetrating radar (GPR) is valuable for the detection of subsurface objects with little or no metal content, such as plastics, ceramics, and concrete piping. However, the effects of antenna configuration parameters, such as height and angle, are not well studied for all sensing applications. GPR simulations and laboratory GPR experiments are performed to evaluate the effects of antenna angle and height on the sensitivity of bistatic air-launched GPR, to search for buried nonmetallic objects. The results presented provide guidance for the development of air-launched GPR systems installed on unmanned aerial vehicles for in-flight subsurface scanning of buried targets.
This paper explores a low-rank and sparse representation based technique to remove the clutter produced by rough ground surface for air-coupled ground penetrating radar (GPR). For rough ground surface, the surface clutter components in different A-Scan traces are not aligned on the depth axis. To compensate for the misalignment effect and facilitate clutter removal, the A-Scan traces are aligned using cross-correlation technique first. Then the low-rank and sparse representation approach is applied to decompose the GPR data into a low-rank matrix whose columns record the ground clutter in A-Scan traces upon alignment adjustment, and a sparse matrix that features the subsurface object under test. The effectiveness of the proposed clutter removal method has been evaluated through simulations.
Ground penetrating radar (GPR) has been shown to be an effective device for detecting buried objects that have little or no metal content, such as plastic, ceramic, and concrete pipes. In this paper, buried non-metallic object detection is evaluated for different antenna elevation angles and heights using a bistatic air-launched GPR. Due to the large standoff distance between antennas and the ground surface, the air-launched GPR has larger spreading loss than the hand-held GPR and vehicle-mounted GPR. Moreover, nonmetallic objects may have similar dielectric property to the buried medium, which results in further difficulty for accurate detection using air-launched GPR. To study such effects, both GPR simulations and GPR laboratory experiments are performed with various setups where antennas are placed at different heights and angles. In the experiments, the test surface areas are configured with and without rocks in order to examine surface clutter effect. The experimental results evaluate the feasibility and effectiveness of bistatic air-launched GPR for detecting buried nonmetallic objects, which provide valuable insights for subsurface scanning with unmanned aerial vehicle (UAV) mounted GPR.
This paper outlines and discusses a few associated details of a smart cities approach to the mapping and condition assessment of urban underground infrastructure. Underground utilities are critical infrastructure for all modern cities. They carry drinking water, storm water, sewage, natural gas, electric power, telecommunications, steam, etc. In most cities, the underground infrastructure reflects the growth and history of the city. Many components are aging, in unknown locations with congested configurations, and in unknown condition. The technique uses sensing and information technology to determine the state of infrastructure and provide it in an appropriate, timely and secure format for managers, planners and users. The sensors include ground penetrating radar and buried sensors for persistent sensing of localized conditions. Signal processing and pattern recognition techniques convert the data in information-laden databases for use in analytics, graphical presentations, metering and planning. The presented data are from construction of the St. Paul St. CCTA Bus Station Project in Burlington, VT; utility replacement sites in Winooski, VT; and laboratory tests of smart phone position registration and magnetic signaling. The soil conditions encountered are favorable for GPR sensing and make it possible to locate buried pipes and soil layers. The present state of the art is that the data collection and processing procedures are manual and somewhat tedious, but that solutions for automating these procedures appear to be viable. Magnetic signaling with moving permanent magnets has the potential for sending lowfrequency telemetry signals through soils that are largely impenetrable by other electromagnetic waves.
In this paper, an object characterization method based on neural networks is developed for GPR subsurface imaging. Currently, most existing studies demonstrate detecting and imaging objects of cylindrical shapes. While in this paper, no restriction is imposed on the object shape. Three neural network algorithms are exploited to characterize different types of object signatures, including object shape, object material, object size, object depth and subsurface medium’s dielectric constant. Feature extraction is performed to characterize the instantaneous amplitude and time delay of the reflection signal from the object. The characterization method is evaluated utilizing the data synthesized with the finite-difference timedomain (FDTD) simulator.
Crack detection is an important application for Ground penetrating radar (GPR) to examine the concrete road or building structure conditions. The layer of rebars or utility pipes that typically exist inside the concrete structure can generate stronger scattering than small concrete cracks to affect detection effectiveness. In GPR image, the signature patterns of regularly distributed rebars or pipes can be deemed as correlated background signals, while for the small size cracks, their image features are typically irregularly and sparsely distributed. To effectively detect the cracks in concrete structure, the robust principal component analysis algorithm is developed to characterize the rank and sparsity of GPR image. For performance evaluations, simulations are conducted with various configurations.
Railroad ballast inspection is critical for the safety of both passenger and freight rail. Ground-penetrating radar (GPR) has been utilized as a highly efficient nondestructive evaluation and structural health monitoring technique in bridge and roadway inspection for many years. However, the development of robust GPR technologies for railroad ballast inspection is still at its early stage due to the complex scattering characteristics of ballast and the lack of efficient algorithms to process big GPR data. An efficient unsupervised method for detecting the region of interest in ballast layer based on the Hilbert transform and Renyi entropy analysis is developed. Both laboratory test and field test are set up and conducted. The data interpretation results demonstrate that the developed region of interest detection algorithm is an efficient and valuable tool for GPR data processing.
In this paper, a method using the instantaneous phase information of the reflection ground penetrating radar (GPR) signal to detect the variation of sand moisture is developed. The moisture changes the permittivity of the medium, which results in different speed when the GPR electromagnetic (EM) wave propagates in the medium. In accordance to this principle, we develop an analytical method to extract GPR reflection signal’s instantaneous phase parameters utilizing Hilbert Transform for sand moisture characterization. For test evaluation, Finite Difference Time Domain (FDTD) numerical simulations using a 3rd party open source program GprMax V2.0, and laboratory experiments on sand samples are conducted using a commercial GPR (2.3 GHz Mala CX) as the data acquisition system.
KEYWORDS: General packet radio service, Orthogonal frequency division multiplexing, Image compression, Synthetic aperture radar, Radar, Compressed sensing, Finite-difference time-domain method, Signal generators, Inspection, Data modeling
This paper presents a new ground penetrating radar (GPR) design approach using orthogonal frequency division multiplexing (OFDM) and compressive sensing (CS) algorithms. OFDM technique is applied to leverage GPR operating speed with multiple frequency tones transmission and receiving concurrently, and CS technique allows utilizing reduced frequency tones without compromising data reconstruction accuracy. Combination of OFDM and CS boosts the radar operating efficiency. For GPR image reconstruction, a synthetic aperture radar (SAR) technique is implemented.
KEYWORDS: Signal processing, General packet radio service, Ground penetrating radar, Inspection, Field programmable gate arrays, Filtering (signal processing), Mathematical modeling, Image enhancement, Signal attenuation, Detection and tracking algorithms
This paper focuses on new signal processing algorithms customized for an air coupled Ultra-Wideband (UWB) Ground Penetrating Radar (GPR) system targeting highway pavements and bridge deck inspections. The GPR hardware consists of a high-voltage pulse generator, a high speed 8 GSps real time data acquisition unit, and a customized field-programmable gate array (FPGA) control element. In comparison to most existing GPR system with low survey speeds, this system can survey at normal highway speed (60 mph) with a high horizontal resolution of up to 10 scans per centimeter. Due to the complexity and uncertainty of subsurface media, the GPR signal processing is important but challenging. In this GPR system, an adaptive GPR signal processing algorithm using Curvelet Transform, 2D high pass filtering and exponential scaling is proposed to alleviate noise and clutter while the subsurface features are preserved and enhanced. First, Curvelet Transform is used to remove the environmental and systematic noises while maintain the range resolution of the B-Scan image. Then, mathematical models for cylinder-shaped object and clutter are built. A two-dimension (2D) filter based on these models removes clutter and enhances the hyperbola feature in a B-Scan image. Finally, an exponential scaling method is applied to compensate the signal attenuation in subsurface materials and to improve the desired signal feature. For performance test and validation, rebar detection experiments and subsurface feature inspection in laboratory and field configurations are performed.
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