Civil Aviation GNSS Interference Detection and Location Based on Genetic Algorithm Using ADS-B Data

Jinqi Li, Hongxia Wang, Zhiqiang Dan, Jiahao Xu, Zhipeng Wang, Yanbo Zhu

Peer Reviewed

Abstract:	According to the 41st session of the ICAO assembly, the very low strength of GNSS signals received from satellites makes GNSS vulnerable to radio frequency interference (RFI), and other undesirable disturbances, which poses a threat to aviation safety. In the field of civil aviation, the performance of GNSS is directly related to NACp, NUCp and other data widely used in Automatic Dependent Surveillance-Broadcast (ADS-B) system, where GNSS RFI can be reflected and located by their numerical changes. Interference detection and location based on ADS-B is a new way to solve GNSS RFI problem in civil aviation. Current researches focus on improving the performance of the algorithm, but there are few studies on the impact mechanism of GNSS RFI. This paper analyzes the influence of GNSS RFI on ADS-B data in principle, and provides theoretical support for algorithm, detects and locates RFI using genetic algorithm. The performance of the proposed detection and location algorithm is verified with the interference events in Chengdu, China in March 2021. It has made an attempt to the application of civil aviation surveillance and navigation fusion and to deal with GNSS RFI. This research provides theoretical reference and technical support for the detection and location of GNSS RFI in civil aviation.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	4168 - 4182
Cite this article:	Li, Jinqi, Wang, Hongxia, Dan, Zhiqiang, Xu, Jiahao, Wang, Zhipeng, Zhu, Yanbo, "Civil Aviation GNSS Interference Detection and Location Based on Genetic Algorithm Using ADS-B Data," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 4168-4182. https://doi.org/10.33012/2023.19392
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