Real-time precise positioning depends on correction products that refine satellite orbit, clock, and bias information. But when those products contain hidden errors, positioning quality can degrade rapidly, and conventional monitoring often misses the problem until ambiguities have already been fixed. This study introduces a new quality-control framework that spots those errors earlier and more completely by combining float ambiguity deviations with ambiguity-float phase residuals. Together, the two signals reveal both stable and fast-changing product faults. The method not only detects unreliable corrections, but also recovers usable information from products that older approaches would simply discard, improving the reliability and availability of real-time high-precision positioning.
State Space Representation (SSR) products are essential for real-time Precise Point Positioning with Ambiguity Resolution (PPP-AR), yet their quality can fluctuate in ways that are difficult to capture. Existing network-based monitoring mainly relies on phase residuals after ambiguities are fixed, which leaves a major blind spot when ambiguities remain float. In practice, such ambiguity-float products are often labeled unreliable and excluded from use, even though some still contain valuable correction information. That conservative strategy reduces satellite availability and can weaken positioning performance in difficult environments such as urban canyons or severe signal blockage. Due to these challenges, a more complete approach was needed to monitor, interpret, and correct SSR product errors.
Researchers from Chang'an University, Universitat Politecnica de Catalunya, and the University of Calgary reported (DOI: 10.1186/s43020-026-00195-y) in Satellite Navigation in 2026 that their new float-ambiguity deviation and phase residual-based quality monitoring and correction (FAP-QMC) framework can evaluate both ambiguity-float and ambiguity-fixed SSR products within a unified workflow, while improving ambiguity fixing reliability, convergence, and positioning accuracy.
The method is built on a key insight: in float Precise Point Positioning (PPP), SSR product errors split into two parts. Stable or slowly varying errors are absorbed into float ambiguities, while abrupt or changing errors remain in phase residuals. FAP-QMC captures both. It first runs PPP float solutions across multiple monitoring stations, then extracts float ambiguity deviations and ambiguity-float phase residuals for each satellite. To avoid contamination from local noise, the framework applies inter-station screening using statistical filtering and density-based clustering before estimating common-mode corrections across the wide-area network. Those corrections are then fed back into the original products, while the remaining phase residuals serve as a unified quality indicator. Using one month of CNES real-time Global Positioning System (GPS) and Galileo SSR products, the team showed that the method improved the ambiguity fixing rate to 95.56%, compared with 92.83% for the conventional phase-residual strategy, while cutting the incorrect fixing rate from 0.69% to 0.09%. During incorrect-fix periods, three-dimensional root mean square error (RMSE) dropped from 15.1 cm to 4.6 cm. The method also improved convergence, with 89.97% of runs reaching first correct fix within 20 minutes.
The authors said the main advance is that ambiguity-float products no longer have to be treated as a black box or thrown away by default. The new framework shows these products can still support reliable positioning when their hidden errors are properly separated, checked across a network, and corrected before use. In that sense, SSR quality management becomes more than fault detection alone: it becomes a way to rescue useful corrections, reduce false fixes, and keep high-precision positioning working under tougher real-world conditions.
The implications are broad for applications that need trustworthy real-time positioning, including precise navigation, deformation monitoring, and operations in signal-challenged environments. The paper also tested the method under satellite-limited conditions designed to mimic tunnel passages or severe urban obstruction. Even there, FAP-QMC outperformed the comparison method, achieving a higher ambiguity fixing rate of 66.26% versus 48.43%, a lower incorrect fixing rate of 10.34% versus 22.66%, and a shorter median time-to-first-fix of 27.43 minutes versus 34.53 minutes. These results suggest the framework could help make next-generation PPP-AR services more resilient where satellite visibility is incomplete and reliability matters most.
