Geometry-Based Discrimination of Authentic and Spoofed GNSS Signals Using Double Differencing

Jade Babcock-Chi, Todd Walter

Peer Reviewed

Abstract:	GNSS spoofing and meaconing pose a growing threat to safety-critical systems reliant on GNSS for positioning, navigation, and timing. This work presents a pseudorange double-difference framework for discriminating between authentic and spoofed GNSS signals across a network of spatially separated receivers, and demonstrates its application to spoofer localization via time-difference-of-arrival (TDOA). Three C/N0-based noise models are empirically derived using a code-minus-carrier observable, and a geometric separation condition is enforced to ensure reliable hypothesis discrimination between all-authentic and all-spoofed signal quartets. The methodology is validated against three spoofing scenarios from Jammertest 2025 using GPS L1 C/A and Galileo E5a measurements across a network of ten receivers, successfully localizing a close-proximity spoofer to within a few meters and recovering directionally consistent transmitter estimates for more geometrically challenging configurations. Limitations and directions for future work are discussed, with emphasis on a network-wide joint classification and localization framework to build upon the current quartet-based enumeration.
Published in:	Proceedings of the ION 2026 Pacific PNT Meeting April 13 - 16, 2026 Hilton Waikiki Beach Honolulu, Hawaii
Pages:	756 - 771
Cite this article:	Babcock-Chi, Jade, Walter, Todd, "Geometry-Based Discrimination of Authentic and Spoofed GNSS Signals Using Double Differencing," Proceedings of the ION 2026 Pacific PNT Meeting, Honolulu, Hawaii, April 2026, pp. 756-771. https://doi.org/10.33012/2026.20603
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