The Use of Artificial Intelligence in Enhancing Navigation Safety in Case of GNSS Signal Fading or Interference

• Autorzy: Chrzan Marcin

Identyfikatory

DOI

10.12716/1001.19.02.31

• Języki publikacji: EN

Abstrakty

EN

Global Navigation Satellite Systems (GNSS) play a key role in modern navigation and transportation service delivery. Today it is difficult to imagine the daily operation of land, sea or air transportation without the positioning and timing provided by satellite systems [6, 8, 11]. In civil aviation, GNSS provides precision landing approaches and support for safety systems, in maritime navigation it enables open sea course determination and maneuvering, and on land it directs vehicle navigation and synchronization of telecommunications networks. Many of these applications are so-called PNT (Positioning, Navigation and Timing) systems, where uninterrupted and accurate position and timing information is critical to safe operations [8]. Unfortunately, satellite systems have recently become the target of hacking attacks. This paper will present AI methods for detecting GNSS anomalies, the role of inertial systems as an independent navigation source, and position correction techniques (e.g., using Kalman filters) supported by intelligent algorithms. An overview of current research and experiments in this field will also be presented, as well as conclusions on the effectiveness and development prospects of the technologies discussed.

Słowa kluczowe

EN

Global Positioning System; GPS; GLONASS; Głobalnaja Nawigacyonnaja Sputnikowaja Sistiema; Galileo; BeiDou; GNSS; Global Navigation Satellite System; spoofing; jamming; satellite navigation security

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2025
• Tom: Vol. 19 No. 2
• Strony: 589--597
• Opis fizyczny: Bibliogr. 15 poz., tab.

Twórcy

autor

Chrzan Marcin

• Kazimierz Pulaski University in Radom, Radom, Poland

• https://orcid.org/0000-0003-4181-4847

Bibliografia

• [1] Bonar B., Buratowski T.: Extended Kalman filter supported by logical conditions for inertial navigation. W: Systemy mechatroniczne : zagadnienia wybrane : praca zbiorowa / pod red. Michała Mańka. — Kraków : Katedra Robotyki i Mechatroniki. Akademia Górniczo-Hutnicza w Krakowie, 2022. — ISBN: 978-83-965598-2-1. — S. 51–59.
• [2] Boguspayev, N., Akhmedov, D., Raskaliyev, A., Kim, A., Sukhenko, A. (2023): A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications. Applied Sciences, 13(8), 4819. https://doi.org/10.3390/app13084819
• [3] Britannia P&I Club - Global Marine P&I Insurance. https://britanniapandi.com/wp-content/uploads/2024/10/Navigational-Risks-at-Sea-Jamming-and-Spoofing.pdf [C: 16 October 2024]
• [4] Clausnitzer, Ch., Alexander K,.(2024): GNSS Jamming/Spoofing and Continued Operational Safety International Committee on GNSS Interference Detection &Mitigation Workshop 15 April 2024
• [5] Chen, L., Liu, Z., Fang, J.(2022): A novel hybrid observation prediction methodology for bridging GNSS outages in INS/GNSS systems. Journal of Navigation. 2022;75(5):1206-1225. doi:10.1017/S037346332200025X
• [6] Chrzan, M., Jackowski, S. (2016): Współczesne systemy satelitarne w transporcie kolejowym. Politechnika Radomska im. Kazimierza Pułaskiego w Radomiu
• [7] EASA partners with IATA to counter aviation safety threat from GNSS spoofing and jamming | EASA. (b. d.). EASA. https://www.easa.europa.eu/en/newsroom-and-events/press-releases/easa-partners-iata-counter-aviation-safety-threat-gnss-spoofing#:~:text=“GNSS%20systems%20offer%20tremendous%20advantages,is%20a%20priority%20for%20the] – [C: 10.03.2025]
• [8] Ghanbarzade, A., & Soleimani, H. (2025): GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning. arXiv preprint arXiv:2501.02352.
• [9] Mahdi AE, Azouz A, Noureldin A, Abosekeen A.(2024): A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments. Sensors (Basel). 2024 Mar 20;24(6):1985. doi: 10.3390/s24061985.
• [10] Pardhasaradhi, B., Yakkati, R. R. Cenkeramaddi, L. R.(2022): Machine Learning-Based Screening and Measurement to Measurement Association for Navigation in GNSS Spoofing Environment in IEEE Sensors Journal, vol. 22, no. 23, pp. 23423-23435, 1 Dec.1, 2022, doi: 10.1109/JSEN.2022.3214349
• [11] Radoš, K., Brkić, M., & Begušić, D. (2024): Recent Advances on Jamming and Spoofing Detection in GNSS. Sensors, 24(13), 4210. https://doi.org/10.3390/s24134210
• [12] Semanjski, S., Semanjski, I., De Wilde, W., & Muls, A. (2020): Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I. Sensors, 20(4), 1171. https://doi.org/10.3390/s20041171
• [13] Simon, D., "Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches", John Wiley & Sons 2006.
• [14] Titterton, D. H. i Weston, J. L., (2004): Strapdown Inertial Navigation Technology, Radar , Sonar and Navigation Series 17. 2004.
• [15] Zhang, Y., A (2019): Fusion Methodology to Bridge GPS Outages for INS/GPS Integrated Navigation System. IEEE Access. 2019;7:61296–61306. doi: 10.1109/ACCESS.2019.2911025

Uwagi

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