Novel Micro-UAV Identification Approach Assisted by Combined RF Fingerprint

In response to the threat to societal security posed by unauthorized unmanned aerial vehicles (UAVs), there is an urgent need for a fast and accurate method of identifying UAVs. The detection and identification of UAVs based on radio frequency (RF) fingerprint features are considered crucial for addressing this issue. This article presents a model for identifying micro-UAV targets based on RF fingerprint information. The system aims to effectively address the challenges of UAV safety management by providing reliable support for UAV control and management. In this model, based on the collected UAV RF signals in the environment, the six statistical RF features of instantaneous amplitude and fractal dimension (FD) in the time-domain information are then extracted, resulting in the combined 7-D RF fingerprints of the UAV. The 7-D RF fingerprints are then reduced to three dimensions using the t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) methods. Finally, the reduced dimensional data are fed into three machine learning algorithms: support vector machine (SVM), weighted K-nearest neighbor (WKNN), and random forest (RandF). The identification results are as follows: in the self-constructed UAV RF dataset, the median identification accuracy using SVM, WKNN, and RandF is up to 98.06%, 99.64%, and 99.63%, respectively. In the open-source UAV RF dataset, the median identification accuracy for SVM, WKNN, and RandF is up to 95.33%, 98.91%, and 98.91%, respectively. All the above experimental results confirm the reliability and effectiveness of the proposed model described in this article.