A Batch Algorithm for GNSS Carrier Phase Cycle Slip Correction

Signal-phase measurements from global navigation satellite systems (GNSSs) have become an important tool for various remote sensing applications, including measuring ionosphere plasma content, atmospheric radio occultation, and water and ice reflectometry. In these types of scenarios, GNSS signals often experience harsh propagation conditions, such as low signal-to-noise ratios, multipath, and semicoherent scattering. These conditions, in turn, lead to the frequent occurrence of cycle slips, which manifests as persistent discrete changes in the bias of the carrier phase measurement. In order to effectively use the precise GNSS phase measurements under such conditions, we argue that a window of high-rate measurements must be used. In addition, we suggest that enforcing sparsity in the occurrence of detected cycle slips can aid in detection. We, therefore, develop a batch cycle-slip detection and estimation method that is effective and computationally tractable under harsh signal conditions. This work focuses in particular on strong ionosphere scintillation, which is among the most difficult scenarios for estimating cycle slips. We demonstrate the effectiveness of our method on both simulated and real GNSS scintillation datasets, showing around a 90% reduction of slips.