High-precision Global Navigation Satellite System (GNSS) positioning depends on successful carrier-phase ambiguity resolution, but this remains difficult in urban canyons, dense vegetation and other challenging environments. This study introduces a residual-based machine learning (ML) validator that uses features less sensitive to environmental degradation and a compact Multilayer Perceptron (MLP) classifier. Tested on real-world datasets from an Unmanned Ground Vehicle (UGV) and an Intelligent Passenger Car (CAR), the method improves correct ambiguity fixing while sharply reducing wrong fixes. It achieves classification accuracy and precision above 90% on independent tests and supports real-time use, offering a practical path to more reliable high-precision positioning.
Conventional GNSS ambiguity validation often works well in open skies. In challenging environments, however, non-line-of-sight (NLOS) reception, multipath and signal blockage create model-reality mismatches. Model-driven tests such as the R-ratio test and the Fixed Failure-Rate Ratio Test (FFRT) rely on specific assumptions about the underlying GNSS model, and their performance can deteriorate when these assumptions are violated, leading to many wrong or missed fixes Machine learning can capture nonlinear relationships, but earlier models were often trained on benign datasets, depended on conventional statistical indicators, and required substantial computation and memory. These limits have hindered deployment on resource-constrained platforms and in real-time high-precision positioning. Given these challenges, there is a need for in-depth research on lightweight, generalizable and residual-aware validation for GNSS ambiguity resolution.
Researchers from the School of Geodesy and Geomatics at Wuhan University, the Chinese Antarctic Center of Surveying and Mapping, the University of Electronic Science and Technology of China and Baidu Online Network Technology published (DOI: 10.1186/s43020-026-00216-w) the study on 30 September 2026 in Satellite Navigation . The paper presents a residual-based machine learning validator for GNSS ambiguity resolution in challenging environments. It combines residual-based features with a compact Multilayer Perceptron to decide whether a fixed integer ambiguity solution should be accepted.
The validator extracts three key residual-based features: Ambiguity Difference Root Mean Square (ADR), Phase Residuals Root Mean Square (PRR), and Phase Consistency Root Mean Square (PCR). PRR measures post-fit carrier-phase residuals, while PCR checks consistency between frequencies after ambiguities are fixed. In feature-importance tests, PRR and PCR were the most influential, with stronger correlations to wrong/correct labels than conventional indicators. The classifier is a single-hidden-layer MLP with only 16 hidden neurons, a model size of about 2.8 KiB, and an inference time of 28 ms for more than 350,000 samples. On a 260-hour UGV dataset from Suzhou and a 240-km car dataset from Wuhan, it achieved accuracy and precision above 90%. In UGV tests, the average correct fixing rate reached 85.21%, versus 77.26% for FFRT, while the wrong fixing rate fell to 1.42% from 16.37%. In the CAR-S5 urban scenario, the correct fixing rate was 85.15%, an improvement of 10.55% over FFRT, with a wrong fixing rate of 0.92%. In addition, the proposed method requires an average of only 1.244 ms per epoch for ambiguity resolution, compared with more than 5 ms for the other methods. On the public SmartPNT-POS dataset, it reached a 72.40% correct fixing rate and a 4.50% wrong fixing rate, outperforming POSM and FlexRTK.
The authors said the key advance is not simply adding machine learning, but giving it residual-based evidence that remains informative when conventional statistical assumptions break down. They said the most influential features were PRR and PCR, which directly test whether the fixed ambiguities are consistent with the carrier-phase observations. They said the compact MLP was chosen deliberately, because real-time GNSS users need low latency and small memory footprints. They added that the method reduces both missed and wrong fixes, although performance may still degrade under extremely weak observation models or severely biased float solutions.
The validator is designed for real-time, high-precision positioning on platforms with limited computing resources, including autonomous vehicles, unmanned ground vehicles, smart agriculture equipment and other location-based services. By improving ambiguity fixing in urban canyons, dense vegetation and other challenging environments, it could help maintain centimeter-to-decimeter positioning continuity where conventional methods frequently fall back to float solutions or output dangerous wrong fixes. The authors suggest the approach can be extended to Precise Point Positioning with ambiguity resolution (PPP-AR) and PPP-Real-Time Kinematic (PPP-RTK). Future work will add environmental features and exploit the temporal invariance of ambiguities to improve robustness across more diverse conditions. Such advances could support safer navigation and more reliable geodetic, surveying and mapping applications.
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