Deep Learning for RF-based Drone Detection and Identification using Welch's Method

Radio Frequency (RF) combined with the deep learning methods promised a solution to detect the presence of the drones. Indeed, the classical techniques (i.e. radar, vision and acoustics, etc.) suffer several drawbacks such as difficult to detect the small drones, false alarm of flying birds or balloons, the influence of the wind on the performance, etc. For an effective drones's detection, two main stages should be established: Feature extraction and feature classification. The proposed approach in this paper is based on a novel feature extraction method and an optimized deep neural network (DNN). At first, we present a novel method based on Welch to extract meaningful features from the RF signal of drones. Later on, three optimized Deep Neural Network (DNN) models are considered to classify the extracted features. The first DNN model can be used to detect the presence of the drones and contains two classes. The second DNN help us to detect and recognize the type of the drone with 4 classes: A class for each drone and the last one for the RF background activities. In the third model, 10 classes have been considered: the presence of the drone, its type, and its flight mode (i.e. Stationary, Hovering, flying with or without video recording). Our proposed approach can achieve an average accuracy higher than 94% and it significantly improves the accuracy, up to 30%, compared to existing methods.