Next Article in Journal
Energy Versus Throughput Optimisation for Machine-to-Machine Communication
Previous Article in Journal
Preparation of Hydrophobic Film by Electrospinning for Rapid SERS Detection of Trace Triazophos
Previous Article in Special Issue
A Fine-Tuned Positioning Algorithm for Space-Borne GNSS Timing Receivers
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Special Issue on GNSS Data Processing and Navigation

Research Group of Astronomy and Geomatics (gAGE), Department Physics, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4119; https://doi.org/10.3390/s20154119
Submission received: 17 July 2020 / Accepted: 22 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue GNSS Data Processing and Navigation)

Abstract

:
Global Navigation Satellite System (GNSS) data can be used in a myriad of ways. The current number of applications exceed by far those originally GNSS was designed for. As an example, the present Special Issue on GNSS Data Processing and Navigation compiles 14 international contributions covering several aspects of GNSS research. This Editorial summarizes the whole special issue grouping the contributions under four different, but related topics.

1. GNSS Signals

The first stage in GNSS data processing involves acquiring the signals transmitted by the satellites. In this regard, ref. [1] proposed a tensor-based subspace tracking algorithm that mitigates multipath interference on receivers using multiple antennas, suitable for real-time applications. Sometimes, the interference can be intentional, ref. [2] evaluated the factors influencing the jamming on GNSS signals with the focus on high-end geodetic GNSS receivers. Finally, ref. [3] proposed a tracking loop able to perform such tracking of received signals in a highly accurate manner, which ultimately determine the accuracy of the positioning achievable.

2. Atmospheric Modelling

The valuable data on the GNSS signals can be used to study the Earth derive a variety of models. One of the main propagation delays is originated at the upper atmosphere, precisely at the ionosphere. Ref. [4] studied the spatial and temporal variations of the Total Electron Content (TEC) at the Earth poles for one solar cycle.

3. High Accuracy Navigation

The accuracy of the computed coordinates by means of GNSS is enhanced when external information is received and combined with the GNSS measurements. Different strategies were presented to cope with outages occurring in the communication link receiving either the measurements from a reference station [5] or precise satellite orbits and clock corrections [6].
The GNSS measurements at the user are corrected using well-established models. Ref. [7] investigated the effect of using different Antenna Phase Centre (APC) correction models focusing on high-end geodetic receivers. Ref. [8] analyzed the variation of the Differential Code Biases (DCBs) occurring for the Beidou GNSS using a 40 m diameter, low-noise, and high-gain antenna.
Once the GNSS data is corrected, the user applies different algorithms to estimate accurately its Position Velocity and Time (PVT). In the case of receivers on the ground, ref. [9] assessed the efficiency of different filter strategies to perform such estimation, and ref. [10] addressed the variances of the different GNSS constellations to weigh them optimally. Finally, ref. [11] presented PVT based on the integration of GNSS measurements with other sensors, focused on autonomous vehicles.
GNSS can be used to determine the PVT of receivers on board of a satellite. Ref. [12] improved the determination of the orbit of the Gravity Recovery and Climate Experiment (GRACE) twin satellites through a modified clock estimating method. Furthermore, ref. [13] presented a positioning algorithm for space-borne GNSS timing receivers, assessing the Ling Qiao Low Earth Orbit (LEO) Chinese satellite.
In the inverse problem, knowledge of station coordinates can be used to determine the computation of satellite coordinates and associated clock biases, which is critical to the whole GNSS. In this regard, ref. [14] presented a characterization and evaluation of the atomic clocks onboard the Japanese system, namely the Quasi Zenith Satellite System (QZSS).

Funding

Adria Rovira Garcia is a Serra Hunter Lecturer and presently holds a Marie Sklodowska Curie Individual Fellowship titled “High Accuracy Navigation under Scintillation Conditions (NAVSCIN)”.

Acknowledgments

The Editors thank all authors for having considered submitting their research to the present special issue. We acknowledge the work of the anonymous reviewers that provided a rigorous refereeing process. All manuscripts improved thanks to the useful and constructive comments received.

Conflicts of Interest

The Editors declare no conflict of interest in the peer-review process of the articles of the special issue.

References

  1. Garcez, C.; de Lima, D.; Miranda, R.; Mendonça, F.; da Costa, J.; de Almeida, A.; de Sousa, R. Tensor-based subspace tracking for time-delay estimation in GNSS multi-antenna receivers. Sensors 2019, 19, 5076. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Bažec, M.; Dimc, F.; Pavlovčič-Prešeren, P. Evaluating the vulnerability of several geodetic GNSS Receivers under chirp signal L1/E1 jamming. Sensors 2020, 20, 814. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Han, M.; Wang, Q.; Wen, Y.; He, M.; He, X. The application of robust least squares method in frequency lock loop fusion for global navigation satellite system receivers. Sensors 2020, 20, 1224. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Xi, H.; Jiang, H.; An, J.; Wang, Z.; Xu, X.; Yan, H.; Feng, C. Spatial and temporal variations of polar ionospheric total electron content over nearly thirteen years. Sensors 2020, 20, 540. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Du, Y.; Huang, G.; Zhang, Q.; Gao, Y.; Gao, Y. A new asynchronous RTK method to mitigate base station observation outages. Sensors 2019, 19, 3376. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Janicka, J.; Tomaszewski, D.; Rapinski, J.; Jagoda, M.; Rutkowska, M. The prediction of geocentric corrections during communication link outages in PPP. Sensors 2020, 20, 602. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Araszkiewicz, A.; Kiliszek, D.; Podkowa, A. Height variation depending on the source of antenna phase centre corrections: LEIAR25.R3 case study. Sensors 2019, 19, 4010. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Hong, J.; Tu, R.; Zhang, R.; Fan, L.; Zhang, P.; Han, J.; Lu, X. Analyzing the satellite-induced code bias variation characteristics for the BDS-3 Via a 40 m dish antenna. Sensors 2020, 20, 1339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Zheng, Y.; Wang, S.; Wang, S. Effective efficiency advantage assessment of information filter for conventional kalman filter in GNSS scenarios. Sensors 2019, 19, 3858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Li, M.; Nie, W.; Xu, T.; Rovira-Garcia, A.; Fang, Z.; Xu, G. Helmert variance component estimation for multi-gnss relative positioning. Sensors 2020, 20, 669. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Min, H.; Wu, X.; Cheng, C.; Zhao, X. Kinematic and dynamic vehicle model-assisted global positioning method for autonomous vehicles with low-cost gps/camera/in-vehicle sensors. Sensors 2019, 19, 5430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Zhou, X.; Jiang, W.; Chen, H.; Li, Z.; Liu, X. Improving the GRACE kinematic precise orbit determination through modified clock estimating. Sensors 2019, 19, 4347. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Chen, X.; Wei, Q.; Zhan, Y.; Ma, T. A Fine-tuned positioning algorithm for space-borne GNSS timing receivers. Sensors 2020, 20, 2327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Xie, W.; Huang, G.; Cui, B.; Li, P.; Cao, Y.; Wang, H.; Chen, Z.; Shao, B. Characteristics and performance evaluation of QZSS onboard satellite clocks. Sensors 2019, 19, 5147. [Google Scholar] [CrossRef] [PubMed] [Green Version]

Share and Cite

MDPI and ACS Style

Rovira-Garcia, A.; Zornoza, J.M.J. Special Issue on GNSS Data Processing and Navigation. Sensors 2020, 20, 4119. https://doi.org/10.3390/s20154119

AMA Style

Rovira-Garcia A, Zornoza JMJ. Special Issue on GNSS Data Processing and Navigation. Sensors. 2020; 20(15):4119. https://doi.org/10.3390/s20154119

Chicago/Turabian Style

Rovira-Garcia, Adria, and José Miguel Juan Zornoza. 2020. "Special Issue on GNSS Data Processing and Navigation" Sensors 20, no. 15: 4119. https://doi.org/10.3390/s20154119

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop