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

Objective: To explore the molecular mechanism of cleft palate in mice induced by 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD). Methods: The pregnant mice were randomly divided into TCDD-treated group (n=42) and control group (n=42). TCDD-treated group was given by gavage a single dose of TCDD (64 μg/kg) at 8: 00 AM on gestation day 10 (GD10) and the control group was given by gavage the isopyknic corn oil. At GD13-GD15, the fetal mice palate development was observed by HE staining. The mouse embryonic palatal mesenchymal cell proliferation was detected by 5-bromo-2-deoxyuridine (BrdU) immunofluorescence. The localization and expression of maternally expressed gene3 (MEG3) in mouse embryonic palatal mesenchymal cells was detected by situ hybridization and real-time PCR (RT-PCR). The key protein expressions of transforming growth factor-β (TGF-β)/Smad signaling pathway in mouse embryonic palatal mesenchyme were analyzed by Western blotting. The interaction of MEG3 and TGF-β receptor Ⅰ (TGF-βRⅠ) was examined by RNA binding protein immunoprecipitation (RIP). Results: At GD13 and GD14, compared with the control group, the ratio of BrdU-positive cells in the palatal mesenchyme of TCDD-treated fetuses decreased significantly (GD13, t=6.66, P=0.003; GD14, t=6.56, P=0.003). However, at GD15, the ratio of BrdU-positive cells was significantly increased (t=-5.98, P=0.004). MEG3 was mainly expressed in the nuclei of fetal mouse palatal mesenchymal cells, and the expression of MEG3 in TCDD group was significantly increased at GD13, GD14 and GD15(GD13, t=39.28, P =0.012; GD14, t=18.75, P =0.042; GD15, t=28.36, P =0.045). At GD14, TCDD decreased the levels of p-Smad2 and Smad4 in embryonic palate mesenchymal cells (p-Smad2, t=9.48, P=0.001;Smad4, t=63.10, P=0.001), whereas the expression of Smad7 was significantly increased at GD14 (t=30.77, P<0.001). The results of the RIP experiment showed that the amount of TGF-βRⅠ-bound MEG3 in mouse embryonic palatal mesenchymal cells in the TCDD group (23.940±1.301) was higher than that in the control group (8.537±1.523)(t=24.55, P<0.001). Conclusions: MEG3 is involved in the suppression of mouse embryonic palatal mesenchymal cell proliferation, functioning at least in part via interacting with the TGF-βRⅠ protein and thereby suppressing Smad signaling in the context of TCDD induced cleft palate.

[Mechanism of cleft palate in C57BL/6N mice induced by 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin].

Abstract. Nowadays, the Global Navigation Satellite System (GNSS) is widely used in many applications. As a new system, China's BeiDou navigation system (BDS) is emerging in recent years. The last satellite of the third generation global BeiDou navigation system (BDS-3) has been successfully launched on June 23 in 2020, which means that BDS can offer navigation services worldwide. We evaluate the quality of BDS signals and analyze the performance of BDS positioning based on the smartphone in Espoo, Finland. The static and kinematic experiments were implemented in the parking lot of the Finish Geospatial Research Institute (FGI) and a highway route in Espoo, respectively. Experimental results show that BDS has good satellite visibility and geometric distribution. The signal carrier-to-noise density ratio (C/N0) of BDS-2 reaches 34.22 dB-Hz, which is comparable to the Global Positioning System (GPS). However, the signal carrier-to-noise density of BDS-3 is slightly lower than BDS-2, which is due to the significant number of BDS-3 satellites at low elevation angles. The horizontal precision of BDS positioning in the static and kinematic experiment is comparable to GPS in the east direction and slightly inferior to GPS in the north direction. However, the BDS shows poor precision in the up direction. In addition, the integration of BDS with other GNSS systems can significantly improve the positioning precision. This study intends to provide a reference for further research on the BDS global Positioning, Navigation, and Timing (PNT) services, particularly for LBS and smartphone positioning.


The performance analysis of bds positioning in nordic areas based on the smartphone

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

[Mechanism of cleft palate in C57BL/6N mice induced by 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin].

The performance analysis of bds positioning in nordic areas based on the smartphone