Improved Drone Classification and Detection Using RF: A Cascaded Deep Learning Approach

In this research, we present a cascaded deep learning (DL) model to identify multiple drone radio frequency (RF) signals, leveraging the latest developments in DL technology. The model consists of a bi-directional long short-term memory (Bi-LSTM) model and a 1D-CNN. In this simulation experiment, the DroneRC dataset is employed. The raw RF data is initially preprocessed using the short-time Fourier transform and the power spectral density technique to identify the most relevant properties before being utilized to train the DL model. The simulation results demonstrate that the proposed DL model exhibits a low error rate and excellent classification accuracy.