UAV-based radio frequency spectrum sensing using FPGA and radio frequency modulation classification

Abstract

This thesis is divided in two stages. The first stage showcases a methodical implementation for a real-time RF emission acquisition, storage, and analysis system, useful for a variety of uses, including spectrum sensing, RF fingerprinting, commercial communication, electronic warfare, and more. The receiving antenna, low noise amplifiers, signal mixers, band-pass filters, and other components make up the analog front end of an RF signal acquisition chain. In order to process the continuous input RF signal from the front-end using digital computing devices such as a CPU (such as a Raspberry Pi SBC) and FPGA (such as a Xilinx ZCU-104), an ADC in the system backend transforms it into digital form. The Red Pitaya STEM Lab 125-10 serves as the main signal collection device in this project, which simulates an electronic warfare system and uses a small signal acquisition and recording system with a simple analog front-end. A Raspberry Pi 3B+ (Single Board Computer) receives the obtained samples for software-based signal processing and analysis. The Red Pitaya’s RF input "IN1" has a 50 MHz LPF and a preset sampling rate of 125 MSPS. Strategic and commercial enterprises will employ the system, which consists of the Red Pitaya, Raspberry Pi, and antenna front-end, for remote data collecting applications using drones, unmanned vehicles, and other devices.