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Special Issue "International GNSS Service Validation, Application and Calibration"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 15 October 2023 | Viewed by 2731

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Special Issue Editors

Prof. Dr. Serdjo Kos

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Guest Editor

Faculty of Maritime Studies, University of Rijeka, Studentska 2, 51000 Rijeka, Croatia
Interests: GNSS; space weather; satellite positioning errors; GNSS risk assessment; inonospheric monitoring for GNSS; GeoRSS systems and technologies

Prof. Dr. Juan F. Prieto

E-Mail Website
Guest Editor

Departamento de Ingeniería Topográfica y Cartografía, Universidad Politécnica de Madrid, Madrid, Spain
Interests: geodesy; InSAR; GNSS; deformation modeling; natural and anthropogenic hazards; engineering geodesy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. José Fernández

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Guest Editor

Instituto de Geociencias IGEO (CSIC-UCM), Madrid, Spain
Interests: geodesy; InSAR; GNSS; deformation modeling; natural and anthropogenic hazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Global Navigation Satellite System (GNSS) is a collection of satellites that are positioned in a specific way to produce and transmit location, timing, and navigation data from space to connected sensors on Earth. Additionally, they offer several distinguishing features, such as the utilization of L-band frequencies, which are particularly well suited to use in remote sensing. It has been proven that GNSS remote sensing can be utilized as a substitute for passive remote sensing.

GNSS calibration is required to make sure that the data and outcomes are accurate. In essence, GNSS site calibration creates the link between the required local northing, easting, and elevation and the WGS84 latitude, longitude, and ellipsoidal height. Typically, site calibration entails both a horizontal and vertical adjustment.

The goal of this Special Issue of Remote Sensing is to provide researchers with a venue to share ground-breaking research that pushes the limits of using real-time GNSS in a variety of applications, as well as validation and calibration techniques. The following are just a few examples of potential topics:

GNSS precise positioning applications in geodesy;
GNSS signal processing and calibration;
Precise non-linear motion modelling of GNSS reference stations and their physical mechanisms;
Aided real-time GNSS precise positioning services and sensor fusion in challenging environments;
Identification of GNSS error sources and mitigation mechanisms;
GNSS augmentation systems and integrity monitoring;
Real-time GNSS precise positioning services with smartphones;
Geohazard monitoring of volcanos, earthquakes, subsidence and landslides;
Connected and autonomous vehicles;
Integrated applications of BIM and digital twins in infrastructure;
Monitoring the Earth’s ionosphere and troposphere;
Monitoring deformations of the solid Earth and variations in the hydrosphere;
Time and frequency transfer;
Earth rotation;
Atmospheric parameters;
Supporting geodetic research.

Prof. Dr. Serdjo Kos
Prof. Dr. Juan F. Prieto
Prof. Dr. José Fernández
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

GNSS technique/technology
GNSS positioning error budget
GNSS risk assessment
Space weather impact on GNSS
GNSS satellite orbit determination
GNSS data validation
GNSS applications on environments including water vapor, water level, and underwater surveying
GNSS applications in disasters such as fires and oil spills
GNSS applications on infrastructures
GNSS time and frequency transfer
GNSS atmospheric parameters
GNSS geodetic research
GNSS interferometric reflectometry applications

Published Papers (4 papers)

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Research

Open AccessArticle

The CNES Solutions for Improving the Positioning Accuracy with Post-Processed Phase Biases, a Snapshot Mode, and High-Frequency Doppler Measurements Embedded in Recent Advances of the PPP-WIZARD Demonstrator

and

Remote Sens. 2023, 15(17), 4231; https://doi.org/10.3390/rs15174231 - 28 Aug 2023

Viewed by 431

Abstract

For many years, the navigation team at the French Space Agency (CNES) has been developing its Precise Point Positioning project. The goal was initially to promote a technique called undifferenced ambiguity resolution. One of the main characteristics of this technique is the capability for a user receiver to perform centimeter-level accuracy in real time. To do so, a demonstrator has been built. Its architecture is composed of three main elements: a correction processing software called the server part, a means to transmit the corrections using standardized messages, and a user software capable of handling the corrections to compute an accurate positioning at the user level. In this paper, we present the recent advances in the CNES precise point positioning demonstrator. They are composed of some evolution of the network of stations and server software, the implementation of the new state space representation standard, a new method for instantaneous ambiguity resolution using uncombined four-frequency signals, its implementation in real-time at the server and the user level, and the use of high-rate Doppler measurements to improve the accuracy of the solution in harsh urban environments. On top of that, the computation of high-accuracy post-processed phase biases with the majority of current GNSS signals supported, compatible with the uncombined method and a new online positioning service to demonstrate the capacity of the user software, is demonstrated. Full article

(This article belongs to the Special Issue International GNSS Service Validation, Application and Calibration)

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Open AccessArticle

The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Jean-Philippe Montillet

Xiaping Ma

and

Zhengkai Huang

Remote Sens. 2023, 15(14), 3572; https://doi.org/10.3390/rs15143572 - 17 Jul 2023

Viewed by 458

Abstract

Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effect on the noise model of GNSS time series, we used four combined noise models to analyze the duration of the time series (ranging from 2 to 24 years) and the data gap (between 2% and 30%) effects on noise model selection and velocity estimation at 72 GNSS stations spanning from 1992 to 2022 in global region together with simulated data. Our results show that the selected noise model have better convergence when GNSS time series is getting longer. With longer time series, the GNSS velocity uncertainty estimation with different data gaps is more homogenous to a certain order of magnitude. When the GNSS time series length is less than 8 years, it shows that the flicker noise and random walk noise and white noise (FNRWWN), flicker noise and white noise (FNWN), and power law noise and white noise (PLWN) models are wrongly estimated as a Gauss–Markov and white noise (GGMWN) model, which can affect the accuracy of GNSS velocity estimated from GNSS time series. When the GNSS time series length is more than 12 years, the RW noise components are most likely to be detected. As the duration increases, the impact of RW on velocity uncertainty decreases. Finally, we show that the selection of the stochastic noise model and velocity estimation are reliable for a time series with a minimum duration of 12 years. Full article

(This article belongs to the Special Issue International GNSS Service Validation, Application and Calibration)

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Open AccessArticle

Forecasting Regional Ionospheric TEC Maps over China Using BiConvGRU Deep Learning

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Remote Sens. 2023, 15(13), 3405; https://doi.org/10.3390/rs15133405 - 05 Jul 2023

Viewed by 506

Abstract

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018. Full article

(This article belongs to the Special Issue International GNSS Service Validation, Application and Calibration)

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Open AccessArticle

A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications

and

Remote Sens. 2023, 15(9), 2439; https://doi.org/10.3390/rs15092439 - 06 May 2023

Cited by 3 | Viewed by 964

Abstract

As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms. Full article

(This article belongs to the Special Issue International GNSS Service Validation, Application and Calibration)

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