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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