The Global Navigation Satellite System (GNSS) technology has received considerable attention in railway train positioning because of the high autonomy of train-borne system. However, performance of GNSS-based train positioning may be greatly affected by GNSS spoofing attacks under complicated operation conditions. In order to mitigate the effects of spoofing on positioning and achieve a trustworthy estimation of the train position, this paper proposes a resilient positioning solution. In the proposed solution, anti-spoofing is realized by spoofing detection using the Spiking Neural Network (SNN), signal parameter estimation through Compressed Sensing (CS)-based sparse reconstruction, and recognition of the spoofing/authentic signals with odometer/trackmap-aided train location prediction. By re-constructing and removing the spoofing signal, and re-tracking the recovered spoofing-free signal, performance of the train position determination can be guaranteed. The proposed solution is assessed through tests under specific GNSS spoofing-injected scenarios. The results demonstrate that the proposed solution can effectively mitigate the negative effects of spoofing on position estimation, and improve the positioning accuracy and availability under spoofing attack conditions.
GNSS Spoofing Detection and Elimination for Resilient Train Positioning Using Spiking Neural Network and Compressed Sensing
24.09.2024
1039299 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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