Autoencoder-Based Deep Learning for UAV RF Signal Recognition and Classification

Unmanned aerial vehicles (UAVs) are increasingly becoming indispensable in various aspects of daily life. However, due to the complexity of network architectures used to differentiate UAV signals from UAV controller signals, distinguishing between UAV models and UAV controller classification through deep learning (DL) has always been a challenging problem. To improve classification accuracy rates, this paper introduces a DL autoencoder-based UAV signal recognition system to classify signals from UAVs and UAV controllers. To be specific, this method uses an encoder-decoder architecture built with a multi-layer neural network, which is divided into two parts: the encoder and the decoder, and integrates residual connections to reduce signal transmission loss. This method performs steady-state slice analysis on the RF signals of the UAV and its controller and performs multiple feature extraction. In terms of distinguishing UAV and controller signals, the method achieved a classification accuracy of 90.94% on the CardRF dataset, a UAV model identification accuracy of 86.95% on the CardRF dataset and 88.75% on the MPACT dataset, and a controller model accuracy of 73.21% on the CardRF dataset and 85.35% on the MPACT dataset.