RF Fingerprinting Unmanned Aerial Vehicles With Non-Standard Transmitter Waveforms
A multi-classifier scheme with a two-step score-based aggregation method, using RF data augmentation to increase neural network robustness to hovering-induced variations, and extending the multi- classifier scheme for detecting a new UAV, not seen earlier during training are proposed.
Abstract
The universal availability of unmanned aerial vehicles (UAVs) has resulted in many applications where the same make/model can be deployed by multiple parties. Thus, identifying a specific UAV in a given swarm, in a manner that cannot be spoofed by software methods, becomes important. We propose RF fingerprinting for this purpose, where a neural network learns subtle imperfections present in the transmitted waveform. For UAVs, the constant hovering motion raises a key challenge, which remains a fundamental problem in previous works on RF fingerprinting: Since the wireless channel changes constantly, the network trained with a previously collected dataset performs poorly on the test data. The main contribution of this paper is to address this problem by: (i) proposing a multi-classifier scheme with a two-step score-based aggregation method, (ii) using RF data augmentation to increase neural network robustness to hovering-induced variations, and (iii) extending the multi-classifier scheme for detecting a new UAV, not seen earlier during training. Importantly, our approach permits RF fingerprinting on manufacturer-proprietary waveforms that cannot be decoded or altered by the end-user. Results reveal a near two-fold accuracy in UAV classification through our multi-classifier method over the single-classifier case, with an overall accuracy of 95% when tested with data under unseen channel. Our multi-classifier scheme also improves new UAV detection accuracy to a near perfect 99%, up from 68% for a single neural network approach.