Durvesh

Enhancing GPS Positioning Accuracy Using Machine Learning

International Journal of Technology Engineering Arts Mathematics Science, Vol. 4, No. 1, pp. 01~06, ISSN: 2583-1224

Addressing the inherent challenges of Global Navigation Satellite Systems (GNSS), this research project introduces an innovative approach by combining Least Square estimation with the Time-Differenced Pseudo Range method to enhance Position, Velocity, and Time (PVT) determination. Enhancing GPS Positioning Accuracy Using Machine Learning Regression aims to improve navigation systems, particularly in challenging environments, through the integration of machine learning within the framework of Least Square estimation. The methodology involves a systematic integration process, showcasing the unique amalgamation of traditional GNSS techniques with intelligent learning through Least Square estimation. The outcomes reveal significant improvements in navigation accuracy, with Random Forest Regression emerging as the most effective algorithm among those explored, maintaining its lead with the lowest MAE of around 0.000122. Haversine distance is employed as a crucial metric for quantitative evaluation. The project's practical implications extend to mitigating delays and errors associated with GNSS, such as atmospheric delays and multipath effects. The results underscore the transformative impact of machine learning algorithms in refining GPS positioning accuracy and set a new benchmark for precision in geospatial analysis and positioning systems. The research concludes by highlighting the project's uniqueness, practical applicability, and real-world adaptability—a tangible solution to persistent challenges in satellite-based navigation.