Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms

Abstract

In the realm of wireless communications, the Global Positioning System (GPS), integral to Global Navigation Satellite Systems (GNSS), finds extensive applications ranging from vehicle navigation to military operations, aircraft tracking, and Geographic Information Systems (GIS). The reliability of GPS is often compromised by errors particularly prevalent in dense and structurally complex environments, where signal attenuation by environmental obstacles like mountains and buildings is common. These challenges necessitate the deployment of high-cost, precision GPS receivers capable of enhanced signal tracking and acquisition. This study investigates the reduction of GPS positioning errors by implementing a machine learning framework, utilizing a dataset from vehicle tracking devices equipped with Novatel and Ublox technologies. Ten machine learning prediction algorithms were evaluated, focusing on techniques that introduce randomness for stability, employ proximity for predictions, incorporate regularization to prevent overfitting, and leverage both single and ensemble methods to refine analyses. Among the evaluated algorithms, the Extra Trees algorithm was distinguished by its superior performance, achieving a coefficient of determination (R²) of 99.6%, with the lowest error rates compared to its counterparts. The errors were quantified as Root Mean Square Error (RMSE) at 1.01E-4, Mean Absolute Error (MAE) at 4.14E-5, and Mean Square Error (MSE) at 1.03E+0 for normalized data. A comparative assessment across ten scenarios demonstrated that the machine learning-enhanced approach deviated by approximately 6.8 meters on average, markedly improving accuracy over traditional GPS methods and reducing positional deviations to a scale of meters. This advance represents a significant stride towards minimizing GPS inaccuracies in complex environments, providing a robust framework for enhancing navigational precision in critical applications.