Real-World ADS-B signal recognition based on Radio Frequency Fingerprinting

被引：15
|
作者：
Zha, Haoran [1 ]
Tian, Qiao [1 ,2 ]
Lin, Yun [1 ]
机构：
[1] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin, Peoples R China
[2] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Peoples R China
来源：
2020 IEEE 28TH INTERNATIONAL CONFERENCE ON NETWORK PROTOCOLS (IEEE ICNP 2020) | 2020年
关键词：
Radio Frequency Fingerprinting; ADS-B; Signal Detection; Deep Learning; CONVOLUTIONAL NEURAL-NETWORKS; DEEP;
D O I：
10.1109/icnp49622.2020.9259404
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
To meet the future needs of increasingly crowded airspace, the International Civil Aviation Organization (ICAO) proposed to use the Automatic Dependent Surveillance-Broadcast (ADS-B) to provide navigation and surveillance technology to solve the problems of security and capacity in the airspace. But ADS-B does not offer any authentication and encryption. So it is vulnerable to attacks by various illegal devices. A novel radiofrequency fingerprint (RFF) recognition method of aircraft identity verification based on deep learning is proposed. The ADS-B signal captured by RTL-SDR is used for confirmation. The experimental results show that the fingerprint is called the Contour Stellar Images with a better recognition effect under different networks and different SNR.
引用
收藏
页数：6
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