Mamba Based GNSS Cycle Slip Detection for the Single Frequency Receiver

Shichuang Nie, Qiaozhuang Xu, Hongzhou Yang

Peer Reviewed

Abstract:	Cycle slips remain a dominant error source in carrier-phase Global Navigation Satellite System (GNSS) positioning and can severely impair accuracy if they are not detected and repaired. Although robust algorithms exist for dual-frequency receivers, low-cost single-frequency units still rely primarily on Doppler-aided techniques, whose performance declines at low sampling rates (e.g., 1 Hz) and whose quality-control mechanisms are rudimentary. This study investigates whether Mamba—a recent sequence-modeling architecture that offers transformer-level contextual capacity with linear complexity—can ameliorate these limitations. We train Mamba to (i) assess the validity of Doppler-aided cycle-slip detections and (ii) estimate potential repair integer. Tests on real GPS L1 data show that the model markedly improves the reliability assessment of Doppler-derived decisions; however, its slip-correction predictions are limited, owing to the weak statistical connection between the input features and the integer nature of cycle slips. These findings highlight both the promise of modern deep-learning models for quality monitoring in low-cost GNSS and the need for richer feature representations to achieve complete cycle-slip correction.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	2671 - 2681
Cite this article:	Nie, Shichuang, Xu, Qiaozhuang, Yang, Hongzhou, "Mamba Based GNSS Cycle Slip Detection for the Single Frequency Receiver," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 2671-2681. https://doi.org/10.33012/2025.20433
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