RF-based drone detection and classification system using convolutional neural network

This paper presents an effort towards developing a detection and classification information system based on the RF signature of several commercial drones. The developed application implements a Convolutional Neural Network which was trained and tested using data from a publically accessible database. The tested neural network reached an accuracy of almost 100% (4-classes), which is considered as a significant contribution to the development of a functional drone detection system. Moreover, the developed interface allows the user to supervise the spectral activity in the 2.4 GHz ISM band, notifies him about the presence and the nature of a potential threat, and stores the event log in a database for later exploitation.