3.1 Machine-Learning-Based Position Error Estimation for Satellite-Based Localization Systems

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

“…Signals coming from satellites could be deflected, diffracted, and blocked on the ground by buildings, trees, and other objects. As a result of the limited satellite sight and multipath impact, the accuracy of location estimation could be significantly reduced [3], [8].…”

A Review of GNSS Carrier Phase Ambiguity Resolution and Conceptual AI-Driven Approaches

“…2023, 54, 12 2 of 9 GNSS measurements are well defined, AI, and particularly its subset Machine Learning (ML), has been an area of increased research interest for GNSS [6]. According to published work in GNSS research, the most typical applications that increasingly make use of ML include satellite signal acquisition exploiting Multi-Layer Perception (MLP), the Convolutional Neural Network (CNN), NLOS (Non-Line of Sight)/multipath/evil waveform detection and classification via Logistic Regression (LR), Support Vector Machines (SVMs), CNN, Recurrent Neural Networks (RNN), etc., included Earth observation/monitoring harnessing the Artificial Neural Network (ANN); GNSS position error estimation utilizing the Long Short-Term Memory (LSTM) network [7]; the detection of falsely resolved integer ambiguities using ANN or CNN [8]; the prediction of ionospheric corrections; the detection of ionospheric scintillation, the improvement of the satellite clock, orbit prediction accuracy, and parameter prediction in missing GNSS corrections [9]; the calibration of the PVT algorithm's filter parameters (e.g., tuning of the Extended Kalman Filter (EKF)) [10]; and the enhancement of the accuracy of outputted PVT information from the positioning module [11] by deploying Reinforcement Learning (RL).…”