Kenneth Bremnes, Rebecca Moen, Sreenivasa Reddy Yeduri, Rakesh Reddy Yakkati, and Linga Reddy Cenkeramaddi, “Classification of UAVs utilizing Fixed Boundary Empirical Wavelet Subbands of RF Fingerprints and Deep Convolutional Neural Network,” has been accepted for publication in IEEE Sensors Journal (2022).
Keywords: RF signals, Drones, Fingerprint recognition, Convolutional neural networks, Autonomous aerial vehicles, Radio frequency, Feature extraction
Abstract: Unmanned aerial vehicle (UAV) classification and identification have many applications in a variety of fields, including UAV tracking systems, antidrone systems, intrusion detection systems, military, space research, product delivery, agriculture, search and rescue, and internet carrier. It is challenging to identify a specific drone and/or type in critical scenarios, such as intrusion. In this article, a UAV classification method that utilizes fixed boundary empirical wavelet sub-bands of radio frequency (RF) fingerprints and a deep convolutional neural network (CNN) is proposed. In the proposed method, RF fingerprints collected from UAV receivers are decomposed into 16 fixed boundary empirical wavelet sub-band signals. Then, these sub-band signals are then fed into a lightweight deep CNN model to classify various types of UAVs. Using the proposed method, we classify a total of 15 different commercially available UAVs with an average testing accuracy of 97.25%. The proposed model is also tested with various sampling points in the signal. Furthermore, the proposed method is compared with recently reported works for classifying UAVs utilizing remote controller RF signals.
More details:DOI: 10.1109/JSEN.2022.3208518