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.
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
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
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