Real-time Precise Point Positioning (RT-PPP) relies heavily on precise Global Navigation Satellite System (GNSS) satellite clock products. Model-based clock estimation can exploit the improved stability of modern atomic clocks to establish and maintain a stable network timescale. However, conventional clock models cannot adequately handle clock anomalies, such as phase and frequency jumps, which can degrade clock estimation accuracy and compromise filter stability. To address this issue, we propose and validate an anomaly-aware real-time satellite clock estimation framework that integrates atomic clock modeling with anomaly identification, isolation, and recovery within a network solution, thereby limiting the propagation of clock anomalies into the network time reference. To further assess the reliability and practical applicability of the framework, the anomaly-confirmation parameters are systematically evaluated, and the computational efficiency is assessed to verify real-time feasibility. Subsequently, validation using actual GNSS data demonstrates the effective identification and mitigation of phase jumps, frequency jumps, and complex anomalies. Real-time service performance evaluation shows that the method achieves comparable or improved clock accuracy across multiple GNSS constellations, while significantly enhancing timescale stability. The proposed approach therefore provides a more stable and resilient real-time GNSS timescale, thereby supporting high-accuracy timing applications.
Abstract Quantifying how land surface properties modulate Global Navigation Satellite System Reflectometry (GNSS-R) coherence is essential for assessing the performance and limitations of GNSS-R land applications. However, these modulations remain poorly characterized at the pan-tropical scale. We develop an explainable machine-learning framework to quantify the pan-tropical land surface modulation of GNSS-R delay–Doppler map (DDM) coherence, including topography, surface water, soil moisture, and vegetation. We represent the coherence with a proxy derived from Cyclone Global Navigation Satellite System (CYGNSS) observations and model it with a Light Gradient Boosting Machine (LightGBM). Under spatial cross-validation, the model explains about 60% of the spatial variance in the coherence proxy and demonstrates reasonable generalization performance under spatial and regional holdout validation. Shapley additive explanations (SHAP) and accumulated local effects (ALE) show that GNSS-R DDM coherence is enhanced over smooth, wet, and moderately vegetated surfaces, but reduced over rough, steep, and densely vegetated terrain. Topographic factors, particularly terrain roughness, emerge as the dominant controls, while surface water occurrence and soil moisture are associated with enhanced coherence across identifiable empirical transition ranges. A dominant-control analysis further reveals distinct regional patterns in which topography, surface water, soil moisture, or vegetation serves as the leading control. Topography dominates about 68% of land, surface water and vegetation each about 14%, and soil moisture about 4%. These results provide a physically meaningful account of where and why land DDM coherence varies, together with an interpretable empirical basis for understanding how land-surface conditions influence DDM coherence and for informing the design of future GNSS-R land applications.
Applications such as the Industrial Internet, Internet of Vehicles, smart cities, and emergency response impose stringent requirements on ubiquitous spatiotemporal services, demanding Positioning, Navigation, and Timing (PNT) capabilities with high precision, low latency, high reliability, and operational stability..The Evolution of Fifth-Generation (5G) mobile communication networks provides a novel technical pathway for the networked, large-scale, and refined operation of BeiDou Navigation Satellite System (BDS) PNT services. In particular, the capabilities of Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine-Type Communications (mMTC) provide important support for low-latency service delivery and massive terminal access. To meet the future demands of massive concurrent terminal access and continuous coverage in complex environments, the existing BDS high-precision service system must overcome three core bottlenecks. First, redundant construction and limited coverage of reference station infrastructure; second, the low efficiency of user-plane point-to-point unicast transmission of augmentation information for massive users; third, insufficient capability for fine-grained monitoring and guarantee of service quality. From the distinctive perspective of 5G control plane capabilities, this paper systematically reviews three key technologies: upgrading integrated BDS/5G base stations, transmitting BDS augmentation information and ensuring service quality through the control plane, and evaluating site selection and deployment strategies for integrated base stations. On this basis, this paper provides an in-depth analysis of the enabling mechanisms and typical implementation pathways of the integrated system across five core domains: positioning, navigation, timing, communication, and sensing. It then examine future trends in integrated communication and navigation and space-ground integrated networks, and identifies key research challenges, including standardization and the development of unified spatiotemporal system The findings of this work provide a comprehensive reference for the system design, standardization, and engineering implementation of BDS-based ubiquitous spatiotemporal networks.
Abstract This study improves the operational eclipse-season yaw attitude model for Global Positioning System (GPS) Block IIF satellites by using Reverse Kinematic Precise Point Positioning (RKPPP) yaw estimates as an observation-driven reference. Using a 7.5-year global GPS tracking dataset, we reconstruct yaw attitude time series over eclipse season maneuvers and compare them with the current operational attitude model. The comparison reveals four recurring discrepancies. Three occur during noon turns: (1) occasional rotation direction mismatches, (2) an inconsistent pre-eclipse yaw evolution that introduces an abrupt change in rotation direction at maneuver onset, and (3) an apparently premature maneuver initiation reflected by large yaw differences. The fourth discrepancy occurs during midnight turns when the $$\beta$$ β angle is close to zero, for which the modeled rotation direction differs from that inferred from RKPPP. To address these issues, we introduce a satellite-specific $$\beta$$ β bias rule for the noon-turn and apply an empirical modified $$\beta$$ β angle prior to eclipse entry to adjust the yaw rate and delay the maneuver onset. For midnight turns at small absolute $$\beta$$ β angle, we propose a decision rule based on the signs of the $$\beta$$ β angle at maneuver start and the $$\beta$$ β rate. When implemented in network orbit and clock estimation, the revised model reduces phase-residual signatures associated with attitude maneuvers and yields smoother, more consistent satellite clock estimates around yaw turns.
Abstract Recent progress in the estimation of all-frequency bias corrections has substantially enhanced Precise Point Positioning with Ambiguity Resolution (PPP-AR), with the potential to achieve instantaneous convergence to centimetre-level accuracy. To investigate the performance of GNSS PPP-AR using the latest all-frequency products from the International GNSS Service (IGS) and to identify the observational factors associated with its performance, this study proposes a machine-learning-driven attribution analysis framework to evaluate PPP-AR performance and quantify the impact of individual observational factors. GPS+Galileo+BDS-3 observations from 440 IGS stations over a 28-day period in 2025 are processed using the uncombined Decoupled Clock Model (DCM). Positioning performance is evaluated in terms of convergence time and converged positioning accuracy. The framework incorporates Random Forest and XGBoost models to identify the observational factors that are most informative for PPP-AR performance. Results show that 98% of solutions achieve instantaneous convergence to a horizontal accuracy of 2.5 cm, while the average converged RMS error reaches 0.8 cm in the horizontal component and 2.0 cm in the vertical component when using the all-frequency products from Wuhan University. Performance discrepancies are observed among different GNSS products, receiver types, and antenna types, which are primarily attributed to differences in Galileo signal availability, bias correction coverage, signal-to-noise ratio levels, and multipath effects. The skyplot entropy is the most informative factor for horizontal positioning accuracy, whereas PDOP and mean satellite elevation are the factors most informative for vertical accuracy. These findings are validated through five-fold cross-validation, demonstrating good model stability and highlighting the potential of data-driven approaches to complement traditional GNSS analysis. In particular, the proposed framework reveals the relationship between observational factors and PPP-AR performance, thereby providing insights for optimizing processing strategies or configuring observation networks to improve PPP-AR positioning performance.
