Unauthorized Amateur UAV Detection Based on WiFi Statistical Fingerprint Analysis

被引：109

作者：

Bisio, Igor ^{[1
,2
]}

Garibotto, Chiara ^{[1
]}

Lavagetto, Fabio ^{[1
]}

Sciarrone, Andrea ^{[1
]}

Zappatore, Sandro ^{[1
]}

机构：

[1] Univ Genoa, Genoa, Italy

[2] People Friendship Univ Russia, Moscow, Russia

来源：

IEEE COMMUNICATIONS MAGAZINE | 2018年 / 56卷 / 04期

关键词：

Aircraft detection - Wireless local area networks (WLAN) - Intrusion detection - Palmprint recognition - Antennas;

D O I：

10.1109/MCOM.2018.1700340

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Amateur drones are enjoying great popularity in recent years due to the wide commercial diffusion of small, rather low-cost devices. More and more user-friendly, easy-to-pilot aerial and terrestrial drones are available off the shelf, and people can even remotely pilot them using their smartphones. This situation brings up the problem of keeping unauthorized drones away from private or sensitive areas, where they can represent a personal or public threat. With this motivation, after a survey of the existing solutions, we propose a WiFi-based approach aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic. This novel detection technique, tested in a variety of real-life scenarios, proved able to efficiently detect and identify intruder drones in all the considered experimental setups, making it a promising unmanned aerial vehicle detection approach in the framework of amateur drone surveillance.

引用

页码：106 / 111

页数：6

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