Machine Learning-Based GNSS Positioning Error Compensation for Static Receivers

Abstract

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of R95 = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications.