GNSS Spoofing and Jammin in London

Zhongxian  Wang

doi:10.54691/61bfk045

Authors

Zhongxian Wang

DOI:

https://doi.org/10.54691/61bfk045

Keywords:

GNSS Interference, GNSS Spoofing and Jamming, Field Trials, London, CTL3510 GNSS Interference Detector.

Abstract

This research provides a thorough examination of GNSS interference, with a focus on multipath effects and interference events. Field trials in London, a global analysis of interference incidents, and a specific exploration of the Chinese context were conducted to gather a diverse dataset. The research aims to enhance our understanding of GNSS interference complexity and its implications. Four main objectives include proficiency in interference measurement devices, rigorous field trials, a global assessment of interference events, and an examination of jamming and spoofing incidents in China. The CTL3510 GNSS Interference Detector was utilized in field trials, capturing interference signals and facilitating real-time monitoring. The study, conducted in high-traffic zones in London, collected data on signal strength, coordinates, and time stamps. The collected data was analyzed to uncover interference patterns, providing valuable insights into the multifaceted nature of GNSS interference and suggesting recommendations for global GNSS navigation system resilience and security.

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References

Dimc, F., et al. (2017). "An Experimental Evaluation of Low‐Cost GNSS Jamming Sensors." Navigation: Journal of The Institute of Navigation 64(1): 93-109.

Enge, P., et al. (2015). "Aviation benefits from satellite navigation." New Space 3(1): 19-35.

Felski, A. (2016). "Methods of improving the jamming resistance of GNSS receiver." Annual of Navigation(23): 185-198.

Ganeshkumar, P., et al. (2016). "A novel jammer detection framework for cluster-based wireless sensor networks." EURASIP Journal on Wireless Communications and Networking 2016: 1-25.

Gecgel, S., et al. (2019). Jammer detection based on artificial neural networks: A measurement study. Proceedings of the ACM Workshop on Wireless Security and Machine Learning.

Hacohen, S., et al. (2020). "Improved GNSS localization and Byzantine detection in UAV swarms." Sensors 20(24): 7239.

Hauschild, A. and O. Montenbruck (2016). "A study on the dependency of GNSS pseudorange biases on correlator spacing." GPS solutions 20: 159-171.

Islam, S., et al. (2023). Impact analysis of spoofing on different-grade GNSS receivers. 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE.

Jagannath, A., et al. (2019). Developing a low cost, portable jammer detection and localization device for first responders. 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE.

Kujur, B., et al. (2023). Experimental Validation of Optimal INS Monitor against GNSS Spoofer Tracking Error Detection. 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE.

Li, J., et al. (2023). "Simultaneous Retrieval of Corn Growth Status and Soil Water Content Based on One GNSS Antenna." Remote Sensing 15(7): 1738.