Adaptive Multiple-Model Kalman Filter for GNSS Carrier Phase and Frequency Estimation Through Wideband Interference

Wengxiang Zhao, Samer Khanafseh, and Boris Pervan

Peer Reviewed

Abstract: Interference events, both intentional and unintentional, are significant threats to global navigation satellite system (GNSS) service continuity. In the presence of interference, it can be difficult for GNSS receivers to maintain continuous tracking of carrier phase and frequency. To address this issue, in place of a traditional phase-locked loop (PLL), we develop and validate a new estimation-based approach to allow GNSS receivers to correctly estimate carrier phase and frequency under conditions of low carrier-to-noise power. An adaptive Kalman filter forms the core of the estimator, with a multiple-model algorithm to account for discrete navigation data bit transitions. The performance of the estimator in the presence of wideband interference is validated through simulation and experiment, showing performance markedly superior to that of a PLL.
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