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A Drone Detection and Inhibition Tool using GNURadio and a Raspberry Pi

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In the recent years, there has been an increase in the demand and use of unmanned aerial vehicles (UAV) know as drones. Even though there are restrictions for its use by licensed drivers and in determined areas, it has been notorious that these devices are used carelessly in public areas putting in risk the integrity of people. In order to address these issues, we propose the creation of a software tool that can help detect drones and block both its communications with the remote controller, and it’s global navigation satellite system (GNSS) capabilities. This solution runs in a Raspberry Pi under an Ubuntu Server installation, and use an external software defined radio (SDR) named HackRF One for both reception (Rx) and transmission (Tx) of radio frequency (RF) signals. This software tool has been developed using Python as the programming language, and GNURadio as the software development kit (SDK) that gives the system the capacbility to execute telecommunications tasks. The solution consists of five Python scripts with its respective user interface for a manual operation. The main script works as a controller for the four remaining RF reception and transmission scripts, and is in charge of providing them with its main execution parameters. It also offers visual tools to explore the RF data generated by the others scripts. The base script is is in charge of obtaining our reference power values for frequencies between 1MHz and 6 GHz. The spectrum scan and band scan scripts are in charge of comparing the real time power values against the reference values, and generate statistics that can help us identify the frequencies in which there is an unusual RF activity. The last script is in charge of generating a noisy signal that can block or interfere communications in different frequency bands where we expect drone RF activity. It was proved that despite the computation limits of a Raspberry Pi, this software can be installed and executed in this computer, scanning the RF spectrum between 1MHz and 6 GHz, and identifying unusual RF activity in frequencies used by drones. It was also proved that a noisy signal generated by our system can indeed interfere or block completely the communications of a drone with its remote controller, and with the GNSS. The solution proposed here is a starting point for a more robust UAV detection and jamming system.
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