Cross-type representation learning in GNSS: a new approach for enhanced SPP positioning through pseudorange residuals

The increasing demand for precision in Global Navigation Satellite Systems (GNSS) positioning underscores the need for advanced methodologies that surpass traditional Standard Point Positioning (SPP) techniques. This research introduces the Cross-Type Satellite Pseudorange Precision (C-SPP) algorithm, a novel approach that enhances SPP accuracy by integrating cross- type representation learning with techniques adapted from natural language processing. By treating environmental conditions and pseudorange residuals as discrete, linguistically analogous entities, the C-SPP algorithm effectively translates complex GNSS data into a structured format conducive to machine learning. This process not only simplifies the data's complexity but also optimizes the algorithm’s adaptability and efficiency through incremental learning. Extensive validation of the C-SPP algorithm across global GNSS stations has demonstrated significant improvements in positioning accuracy and robustness, outperforming traditional methods under diverse geographic and climatic conditions. The algorithm's unique capability to manage and correct pseudorange residuals offers a substantial accuracy advantage without necessitating excessive computational resources, thus maintaining operational efficiency. The findings from this research not only enhance our understanding of the practical applications of machine learning in satellite navigation but also lay the groundwork for future advancements that could extend the reach and efficacy of GNSS technologies.