Abstract Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based, utilizing satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in machine learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS, such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field. 

A survey of machine learning techniques for improving Global Navigation Satellite Systems

Global navigation satellite system (GNSS) positioning accuracy is degraded in urban canyons due to signal blockages and reflections, which is still a major challenge. Recently, using machine learning to improve the accuracy of GNSS positioning in urban areas has become a new trend. This paper summarizes the works focused on GNSS multipath/non-light-of-sight (NLOS) mitigation using machine learning. The review of the studies is categorized based on the input features, algorithms, and outputs. The categorization shows that the received signal strength, elevation angle, and receiver correlator outputs from a single channel of satellite signal are the most popular input features. For the algorithm selection, the support vector machine and fully connected neural network (FCNN) are the algorithms most widely used. In terms of the outputs, most of the works made improvements in measurement status prediction, namely, LOS, multipath, and NLOS. Besides, this paper also provides an open-source dataset with four scenarios for machine learning algorithms for the GNSS multipath/NLOS mitigation. Finally, the benchmarks are established based on the proposed dataset and the FCNN and least-squares estimation to enable performance evaluation in Kaggle. 

Machine Learning in GNSS Multipath/NLOS Mitigation: Review and Benchmark

Artificial intelligence (AI) is revolutionizing numerous fields, with increasing applications in Global Navigation Satellite Systems (GNSS) positioning algorithms in intelligent transportation systems (ITS) via deep learning. However, a significant technological disparity exists as traditional GNSS algorithms are often developed in Fortran or C, contrasting with the Python-based implementation prevalent in deep learning tools. To address this discrepancy, this paper introduces pyrtklib, a Python binding for the widely utilized open-source GNSS tool, RTKLIB. This binding makes all RTKLIB functionalities accessible in Python, facilitating seamless integration. Moreover, we present a deep learning subsystem under pyrtklib, which is a novel deep learning framework that leverages pyrtklib to accurately predict weights and biases within the GNSS positioning process. The use of pyrtklib enables developers to easily and quickly prototype and implement deep learning-aided GNSS algorithms, showcasing its potential to enhance positioning accuracy significantly.

pdf/pyrtklib-an-open-source-package-for-tightly-coupled-deep-k2zlkhmtk1lt.pdf

pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons

A Deep Learning Approach for the Classification of Multipath Ranging Errors in Challenging Urban Environments

Abstract. In urban areas, the None-Line-Of-Sight (NLOS) and Multipath (MP) signals are the major issues degrading the GNSS position accuracy. Signal reception type should be identified before correcting the NLOS or MP induced errors. Signal features, i.e., signal strength, change rate of received signal strength, difference between delta pseudo-range and pseudo-range rate, have been explored in signal reception type classification. In this letter, with the aim to improve the signal classification accuracy, we propose a new GNSS NLOS/MP/LOS signals classification method using the correlator-level measurements. Firstly, vector tracking (VT) is employed to generate correlator-level measurements; secondly, a deep learning method, Convolutional Neural Network (CNN), is employed to automatically extract the features and identify the signal reception type, correlators’ outputs calculated at different code phases are employed as the inputs of the CNN. Field test is carried out for assessing the performance of the proposed method, and the CNN method obtains state-of-art performance compared with the K-nearest Neighbors Algorithm (kNN) and Support Vector Machine (SVM) methods.


Convolutional neural networks based gnss signal classification using correlator-level measurements

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Convolutional neural networks based gnss signal classification using correlator-level measurements