A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Abstract

In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method

“…The effectiveness and robustness of traditional modeling and forecast methods such as fitting and extrapolation, the Grey System Model (GM) [18], and Auto Regressive and Moving Average (ARMA) [19] have been proven. With the recent developments in machine learning, the application of machine learning algorithms to GNSS signal processing has received increasing attention [20]. For better positioning accuracy, the Long Short-Term Memory neural network (LSTM) as a supervised learning model was leveraged for clock bias prediction during GNSS signal outages [21,22].…”

Recent advances in earthquake seismology using machine learning

“…Notably, our focus is on exploring the applications of ML in individual fields rather than providing a comprehensive review of ML applications in the entire field of study. Excellent review articles have been provided for the reader's reference on the recent applications of ML in solid earth geosciences by , seismology by Kong et al (2019) and Beroza (2022, 2023), microseismic monitoring with small signals by Li (2021) and Anikiev et al (2023), and analysis of Global Navigation Satellite System (GNSS) data by Siemuri et al (2022).…”

Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data

“…A vehicle's GNSS trajectory is a record of the vehicle's path, containing rich road information (e.g., lanes, turns, speed limits, road widths, and road intersections) that directly reflects the road network's geometric characteristics and provides a new database for road intersection extraction [8,9]. Therefore, an increasing number of scholars are utilizing vehicle GNSS trajectory data in tandem with machine learning to extract road geometry data and examine vehicle behavior, among other applications [10][11][12][13][14][15][16] The traditional road intersection detection algorithm takes the vehicle trajectory's unique turning information and speed information at the road intersection as the benchmark, extracts the turning points after ensuring the intersection's accuracy, and then extracts the road intersection on the basis of the turning points' clustering. Qixing developed a scale-and orientation-invariant traj-SIFT feature to localize and recognize junctions using a supervised learning framework [17].…”