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GNSS positioning has become a key element in everyday life of millions of people, from the person using google maps to move around an unknown city to the mailman or the DRON pilot who require it to carry out their work. All of them benefit in some way from the GNSS constellations and the position algorithms.The European Union through their GNSS constellation, Galileo, has recently made available a new service called Galileo High Accuracy Service (HAS). With the aim of improving the positioning solutions already provided by the Open Service (OS) to a centimetric level with the target of professional and commercial users requiring this high accuracy. As a result, in this Master Thesis project the steps of the development and implementation of a Software-Defined Radio to collect the High Accuracy corrections transmitted through Galileo GNSS constellation are going to be shown. The SDR itself is going to be made available so that other persons from companies to academia can benefit from it and see how the corrections are extracted and either use the algorithm or implement its own to be able to use this High Accuracy Service.

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Drone-mounted ground penetrating radar (GPR) has the capability to investigate terrain that is inaccessible or hazardous to humans. A linear frequency-modulated (LFM) radar with the potential for GPR applications is described based on the LimeSDR Mini software defined radio (SDR). Challenges of the LimeSDR Mini radar include the SDR's lack of support for transmitter-receiver synchronization and high bleedthrough leakage. These issues are overcome through corrective software processing techniques including deconvolution of the SDR's system impulse response and digital feed-through nulling. Feed-through nulling is effective at reducing bleedthrough leakage, achieving a 26 dB reduction in power. Although high noise can confound the identification of targets with small radar cross sections in dynamic environments, the LimeSDR Mini radar is demonstrated to display a moving target across multiple ranges. This research demonstrates the increasing accessibility of SDR radar for drone applications, as the LimeSDR Mini is lightweight and low-cost compared to high-end SDRs typically used in SDR radar.

Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator and channel state information, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90\% and device localization within 1.10 meters.

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