Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The most appealing performance for the 6-feature vector case is attained by the RandF, managing a recognition rate of 100% in the indoor experiment and 96.26% in the 2 km outdoor experiment. The recognition rate of the four ML classifiers is larger than 95.52% in the 2 km outdoor experiment, which is better than other UAV detection methods such as radar, acoustic and video.