UAV Detection and Identification Based on WiFi Signal and RF Fingerprint

• Published in: IEEE sensors journal Vol. 21; no. 12; pp. 13540 - 13550
• Main Authors: Nie, Wei, Han, Zhi-Chao, Zhou, Mu, Xie, Liang-Bo, Jiang, Qing
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; bispectrum; Feature extraction; Fingerprints; fractal dimension (FD); Fractal geometry; Machine learning; Machine learning algorithms; Principal components analysis; Radio frequency; Radio signals; RF fingerprint; Sensors; UAV detection and identification; Unmanned aerial vehicles; Unmanned aerial vehicles (UAVs); Wavelet transforms; Wireless fidelity
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3068444

The security threats caused by the popularity of Unmanned Aerial Vehicles (UAVs) have received much attention. In this paper, a UAV detection and identification system based on WiFi signal and radio frequency (RF) fingerprint is proposed. The system firstly conducts UAV detection and after the UAV is detected, fractal dimension (FD), axially integrated bispectra (AIB), and square integrated bispectra (SIB) are extracted as UAV RF fingerprints due to their applicability and reliability. Furthermore, we propose weighted AIB and SIB fingerprints to identify UAVs. Since the high dimensionality of AIB and SIB features, the principal component analysis (PCA) algorithm is applied to reduce the dimensionality of these two features. Then the neighborhood component analysis (NCA) algorithm is utilized to weight the dimensions of the two features, AIB and SIB. The extracted UAV fingerprints are stored as training data and test data, respectively. Finally, machine learning algorithms are utilized to identify UAVs. The identification results are as follows: In the indoor scenario, the average identification accuracy of three features (FD, AIB, SIB) are 100%, 97.23%, and 96.11%, respectively. In outdoor scenario, the identification accuracy of three features are 100%, 95.00%, and 93.50%, respectively.