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Sensors 2019, 19(2), 274; https://doi.org/10.3390/s19020274

An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis

1
Institute of Electron Device & Application, Hangzhou Dianzi University, Hangzhou 310018, China
2
Science and Technology on Electronic Test & Measurement Laboratory, The 41st Research Institute of CETC, Qingdao 266555, China
3
Department of Electrical Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
*
Author to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 4 January 2019 / Accepted: 8 January 2019 / Published: 11 January 2019
(This article belongs to the Section Remote Sensors)
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Abstract

Unmanned aerial vehicles (UAVs) are now readily available worldwide and users can easily fly them remotely using smart controllers. This has created the problem of keeping unauthorized UAVs away from private or sensitive areas where they can be a personal or public threat. This paper proposes an improved radio frequency (RF)-based method to detect UAVs. The clutter (interference) is eliminated using a background filtering method. Then singular value decomposition (SVD) and average filtering are used to reduce the noise and improve the signal to noise ratio (SNR). Spectrum accumulation (SA) and statistical fingerprint analysis (SFA) are employed to provide two frequency estimates. These estimates are used to determine if a UAV is present in the detection environment. The data size is reduced using a region of interest (ROI), and this improves the system efficiency and improves azimuth estimation accuracy. Detection results are obtained using real UAV RF signals obtained experimentally which show that the proposed method is more effective than other well-known detection algorithms. The recognition rate with this method is close to 100% within a distance of 2.4 km and greater than 90% within a distance of 3 km. Further, multiple UAVs can be detected accurately using the proposed method. View Full-Text
Keywords: spectrum sensing; radio frequency (RF); singular value decomposition (SVD); spectrum accumulation (SA); statistical fingerprint analysis (SFA) spectrum sensing; radio frequency (RF); singular value decomposition (SVD); spectrum accumulation (SA); statistical fingerprint analysis (SFA)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, S.; Qin, H.; Liang, X.; Gulliver, T.A. An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis. Sensors 2019, 19, 274.

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