Implementación de un sistema autónomo rastreador de transmisiones no deseadas de bajo coste basado en RTL-SDR

Abstract

This graduate degree thesis pertains to the development of an algorithm that scans radio wave spectra within the FM Radio (88 MHz -108 MHz) and TV (UHF, VHF) bandwidths, with the use of software defined radio, specifically using a RTL-SDR Blog V3 device. The data are processed in Python with the use of the Pandas and Numpy Python libraries. These elements together constitute the infrastructure that processes and analyzes the data with the goal of discerning between legal and illegal radio transmissions. Necessary for this process was the use of statistical operators such as correlation as well as signal comparators like the root mean square error, to serve as metrics of the similarity between a scanned signal and a reference signal. All this information is presented through a web browser interface by way of a web interface programmed in Python, HTML and CSS. Within the web interface a user first needs to create an account. Once logged in, the user then selects the scanning bandwidth (FM or TV) in which the system will detect any undesired signals. The web application runs on a Raspberry Pi 3 which acts as the web server. This server then initiates the scanning and data processing commands, as well as connect to a Google Cloud Storage database in order to store any alerts of illegal radio transmissions in the cloud.