Reinforcement Learning-Driven GNSS Observation Selection for Enhanced PPP Accuracy

This work presents a reinforcement learning (RL) framework integrated into GMV’s GSharp® precise point positioning (PPP) algorithm to optimize GNSS measurement processing. Initially developed for multipath mitigation, the RL agent has evolved into a decision-making tool that evaluates the usefulness of GNSS observations to enhance positioning accuracy. The model processes GNSS data epoch by epoch using features such as pseudoranges, signal-to-noise ratios, elevation angles, and residuals. Based on these inputs, the agent decides whether each measurement should be included in the positioning solution. A custom reward function encourages decisions that reduce positioning error while maintaining solution stability. The system was trained on over 50 h of GNSS raw data collected in diverse environments, including urban canyons, suburban areas, and open spaces, promoting generalization across real-world conditions. Preliminary validation shows that the RL-enhanced PPP algorithm achieves accuracy improvements over the baseline GSharp® solution in several challenging scenarios. These results suggest that RL can support GNSS data processing by adaptively managing the quality and relevance of observations, potentially enabling more robust and precise positioning in complex environments.