Classifying UAVs With Proprietary Waveforms via Preamble Feature Extraction and Federated Learning

Abstract

Small unmanned aerial vehicles (UAVs) are deployed in a number of different emerging market segments as well as for recreational hobby flying. Driven by their ubiquitous availability, a large number of manufacturers offer UAV models in different form factors, control and load carrying capacities. This paper proposes a deep learning method to detect the type/model of the UAV using the transmitted RF signals, even when these signals follow proprietary medium access protocols whose headers cannot be decoded. The main contributions are as follows: (i) We show how the preamble portion of the packet is better suited for learning subtle protocol-specific differences, instead of randomly selecting any subset of the transmitted packet, (ii) we propose a pre-processing scheme that generates cross-correlation feature maps to enhance the classification accuracy, (iii) we develop a deep convolutional neural architecture that can be trained in data collected from static scenarios and then tested in practical hovering conditions with 98.2% accuracy of UAV model classification, demonstrating robustness to channel variations, and (iv) we extend this model towards a federated learning paradigm where sensors send individually trained models back to a central controller that combines them, without any appreciable loss of accuracy. Our evaluations are performed on an 8.9 GB dataset collected from static and flying UAVs, which we also release as part of the technical contributions of the work.