Automatic snow layer detection in drone-borne radar data using edge detection and morphology

The thesis aims to detect the primary interfaces in ground-penetrating radar (GPR) data collected from a snow-pack. An airborne drone was used to collect the data, where a 2D image of the substructures was gattered, including GPS and laser altimeter data. Al these were used under the thesis to develop the method or presentation of the results. The method focused on simpler image processing techniques where more complicated methods would be explored if needed. Ground truth was drawn manually with guidance from a GPR expert. The primary method used in this thesis was Canny edge detection and morphological operators. Two different techniques were used to detect the two different layers because they showed significantly different characteristics. The technique for the top layer resulted in a root mean square error (RMSE) accuracy of 5 cm, which was within the range resolution of the radar system was achieved. A quality estimate was also given to the top layer, indicating the top estimate's quality found through our method. The bottom estimate showed an accuracy of 20 cm because of the complexity of the bottom layer. On the other hand, the method did have a cross-correlation of 0.9, meaning it could follow the bottom layer in most datasets, but it could struggle to have the exact location correct. In short, the method presented could be applied routinely to estimate the primary interfaces in other GPR data, where no method previously existed

The development of copper clad laminate horn antennas for drone interferometric synthetic aperture radar

Interferometric synthetic aperture radar (InSAR) is an active remote sensing technique that typically utilises satellite data to quantify Earth surface and structural deformation. Drone InSAR should provide improved spatial-temporal data resolutions and operational flexibility. This necessitates the development of custom radar hardware for drone deployment, including antennas for the transmission and reception of microwave electromagnetic signals. We present the design, simulation, fabrication, and testing of two lightweight and inexpensive copper clad laminate (CCL)/printed circuit board (PCB) horn antennas for C-band radar deployed on the DJI Matrice 600 Pro drone. This is the first demonstration of horn antennas fabricated from CCL, and the first complete overview of antenna development for drone radar applications. The dimensions are optimised for the desired gain and centre frequency of 19 dBi and 5.4 GHz, respectively. The S11, directivity/gain, and half power beam widths (HPBW) are simulated in MATLAB, with the antennas tested in a radio frequency (RF) electromagnetic anechoic chamber using a calibrated vector network analyser (VNA) for comparison. The antennas are highly directive with gains of 15.80 and 16.25 dBi, respectively. The reduction in gain compared to the simulated value is attributed to a resonant frequency shift caused by the brass input feed increasing the electrical dimensions. The measured S11 and azimuth HPBW either meet or exceed the simulated results. A slight performance disparity between the two antennas is attributed to minor artefacts of the manufacturing and testing processes. The incorporation of the antennas into the drone payload is presented. Overall, both antennas satisfy our performance criteria and highlight the potential for CCL/PCB/FR-4 as a lightweight and inexpensive material for custom antenna production in drone radar and other antenna applications

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Radar System Development for Drone Borne Applications with Focus on Snowpack Parameters

A complete representation of the Arctic cryosphere has historically been restricted by its remoteness, large extent, and restrictions in measurement methods and equipment. Here, remote sensing of snow-cover is a central method to improve the current knowledge of the Earth's ecosystem, and hence a critical component in cryospheric models. The use of drone-borne radar systems has seen considerable advances over recent years, allowing for the application of drone-mounted remote sensing of snow properties. This thesis describes the development of an ultra-wideband radar system for drone-mounted snow measurements. From the initial testing and technical implementation to field trials and method development for more advanced radar data analysis. This involves the development of lightweight and high-bandwidth radar systems intending to understand the limitations of design parameters for drone-borne radar systems and how these parameters influence the ability to measure snow conditions. Such understanding includes antenna theory and ultra wide-band radar theory, where most choices involve compromises. Snow as an electromagnetic propagation medium is presented with a focus on the previous design solutions. In that respect, various methods to measure snow parameters are discussed. Furthermore, this thesis aims to describe the iterative process of a drone-borne radar system development and how experiences from field trials are central to further improvements

Drone-Mounted UWB Snow Radar: Technical Improvements and Field Results

Drone borne radar systems have seen considerable advances over recent years, and the application of drone-mounted continuous wave (CW) radars for remote sensing of snow properties has great potential. Regardless, major challenges remain in antenna design for which both low weight and small size combined with high gain and bandwidth are important design parameters. Additional limiting factors for CW radars include range ambiguities and antenna isolation. To solve these problems, we have developed an ultra-wideband snow sounder (UWiBaSS), specifically designed for drone-mounted measurements of snow properties. In this paper, we present the next iteration of this prototype radar system, including a novel antenna configuration and useful processing techniques for drone borne radar. Finally, we present results from a field campaign on Svalbard aimed to measure snow depth distribution. This radar system is capable of measuring snow depth with a correlation coefficient of 0.97 compared to in situ depth probin

„DRONAR – A drone-borne radar for the detection of landmines and booby traps“

Reconnaissance in military and security missions is a basic requirement for their success. Threats such as landmines and booby traps must be efficiently detected in order to ensure the safety and security of people and material. The DRONAR system was developed as a drone-borne radar system for the rapid large-area reconnaissance of areas and marching routes

Automatic snow layer detection in drone-borne radar data using edge detection and morphology

The thesis aims to detect the primary interfaces in ground-penetrating radar (GPR) data collected from a snow-pack. An airborne drone was used to collect the data, where a 2D image of the substructures was gattered, including GPS and laser altimeter data. Al these were used under the thesis to develop the method or presentation of the results. The method focused on simpler image processing techniques where more complicated methods would be explored if needed. Ground truth was drawn manually with guidance from a GPR expert. The primary method used in this thesis was Canny edge detection and morphological operators. Two different techniques were used to detect the two different layers because they showed significantly different characteristics. The technique for the top layer resulted in a root mean square error (RMSE) accuracy of 5 cm, which was within the range resolution of the radar system was achieved. A quality estimate was also given to the top layer, indicating the top estimate's quality found through our method. The bottom estimate showed an accuracy of 20 cm because of the complexity of the bottom layer. On the other hand, the method did have a cross-correlation of 0.9, meaning it could follow the bottom layer in most datasets, but it could struggle to have the exact location correct. In short, the method presented could be applied routinely to estimate the primary interfaces in other GPR data, where no method previously existed

Analysis of Low-Frequency Drone-Borne GPR for Root-Zone Soil Electrical Conductivity Characterization

In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping