An Attention-Based Convolutional Network Framework for Detection and Localization of GNSS Interference Sources

Global navigation satellite system (GNSS) interference severely affects the quality of automatic dependent surveillance-broadcast (ADS-B) data, thereby jeopardizing aviation safety. Therefore, this article proposes an attention-based machine learning methodology for detecting and localizing GNSS int...

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Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 60; H. 3; S. 2995 - 3011
Hauptverfasser:	Cai, Kaiquan, Di, Zuo, Zhu, Yanbo, Zhao, Peng, Shi, Chuang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	ADS-B system Aircraft Algorithms Antenna arrays attention mechanism Automatic dependent surveillance-broadcast (ADS-B) Aviation convolutional neural network detection Feature extraction Global navigation satellite system global navigation satellite system (GNSS) interference Global Positioning System Interference localization Location awareness Machine learning
ISSN:	0018-9251, 1557-9603
Online-Zugang:	Volltext
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