Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Other Publications
    • ion

User menu

  • My alerts

Search

  • Advanced search
NAVIGATION: Journal of the Institute of Navigation
  • Other Publications
    • ion
  • My alerts
NAVIGATION: Journal of the Institute of Navigation

Advanced Search

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Follow ion on Twitter
  • Visit ion on Facebook
  • Follow ion on Instagram
  • Visit ion on YouTube
Research ArticleOriginal Article
Open Access

Regional Ionosphere Delay Models Based on CORS Data and Machine Learning

Randa Natras, Andreas Goss, Dzana Halilovic, Nina Magnet, Medzida Mulic, Michael Schmidt, and Robert Weber
NAVIGATION: Journal of the Institute of Navigation September 2023, 70 (3) navi.577; DOI: https://doi.org/10.33012/navi.577
Randa Natras
1Deutsches Geodätisches, Forschungsinstitut der Technischen Universität München (DGFI-TUM), Department of Aerospace and Geodesy, Technical University of Munich, Munich, 80333, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
Andreas Goss
1Deutsches Geodätisches, Forschungsinstitut der Technischen Universität München (DGFI-TUM), Department of Aerospace and Geodesy, Technical University of Munich, Munich, 80333, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dzana Halilovic
3Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, 1040, Austria
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nina Magnet
2OHB Digital Solutions GmbH, Graz, 8044, Austria
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Medzida Mulic
4Department of Geodesy and Geoinformation, University of Sarajevo, Sarajevo, 71000, Bosnia-Herzegovina
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Schmidt,
1Deutsches Geodätisches, Forschungsinstitut der Technischen Universität München (DGFI-TUM), Department of Aerospace and Geodesy, Technical University of Munich, Munich, 80333, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert Weber
3Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, 1040, Austria
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF
Loading

REFERENCES

  1. ↵
    1. Bergeot, N.,
    2. Chevalier, J.-M.,
    3. Bruyninx, C.,
    4. Pottiaux, E.,
    5. Aerts, W.,
    6. Baire, Q.,
    7. Legrand, J.,
    8. Defraigne, P., &
    9. Huang, W.
    (2014). Near real-time ionospheric monitoring over Europe at the Royal Observatory of Belgium using GNSS data. Journal of Space Weather and Space Climate, 4, A31. https://doi.org/10.1051/swsc/2014028
  2. ↵
    1. Bilitza, D.
    (2018). IRI the international standard for the ionosphere. Advances in Radio Science, 16, 1–11. https://doi.org/10.5194/ars-16-1-2018
  3. ↵
    1. Boisits, J.,
    2. Glaner, M., &
    3. Weber, R.
    (2020). Regiomontan: a regional high precision ionosphere delay model and its application in precise point positioning. Sensors, 20(10), 2845. https://www.mdpi.com/1424-8220/20/10/2845
  4. ↵
    1. Bottou, L.
    (1991). Stochastic gradient learning in neural networks. Proceedings of Neuro-Nimes, 91(8), 12.
  5. ↵
    1. Boussard, M.,
    2. Mars, C.,
    3. Dès, R., &
    4. Chopinaud, C.
    (2017). Periodic split method: learning more readable decision trees for human activities. Conférence Nationale sur les Applications Pratiques de l’Intelligence Artificielle,
  6. ↵
    1. Breiman, L.
    (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
    CrossRef
  7. ↵
    1. Camporeale, E.,
    2. Wing, S., &
    3. Johnson, J. R.
    (2018). Machine learning techniques for space weather. Elsevier. https://doi.org/10.1016/B978-0-12-811788-0.09994-7
  8. ↵
    1. Chapman, S., &
    2. Bartels, J.
    (1962). Geomagnetism. (v.2) Clarendon Press.
  9. ↵
    1. Ciraolo, L.,
    2. Azpilicueta, F.,
    3. Brunini, C.,
    4. Meza, A., &
    5. Radicella, S. M.
    (2007). Calibration errors on experimental slant total electron content (TEC) determined with GPS. Journal of Geodesy, 81(2), 111–120. https://doi.org/10.1007/s00190-006-0093-1
  10. ↵
    1. Covington, A. E.
    (1969). Solar Radio Emission at 10.7 cm, 1947–1968. Journal of the Royal Astronomical Society of Canada, 63, 125–132.
    Web of Science
  11. ↵
    1. Dach, R.,
    2. Lutz, S.,
    3. Walser, P., &
    4. Fridez, P.
    (2015). Bernese GNSS Software Version 5.2. University of Bern, Bern Open Publishing. https://doi.org/10.7892/boris.72297
  12. ↵
    1. El-Diasty, M.
    (2017). Regional ionospheric modeling using wavelet network model. The Journal of Global Positioning Systems, 15(1), 2. https://doi.org/10.1186/s41445-017-0007-y
  13. ↵
    1. Farzaneh, S., &
    2. Forootan, E.
    (2018). Reconstructing regional ionospheric electron density: a combined spherical Slepian function and empirical orthogonal function approach. Surveys in Geophysics, 39(2), 289–309. https://doi.org/10.1007/s10712-017-9446-y
  14. ↵
    1. Feltens, J.
    (2003). The international GPS service (IGS) ionosphere working group. Advances in Space Research, 31(3), 635–644. https://doi.org/10.1016/S0273-1177(03)00029-2
  15. ↵
    1. Goss, A.,
    2. Schmidt, M.,
    3. Erdogan, E.,
    4. Görres, B., &
    5. Seitz, F.
    (2019). High-resolution vertical total electron content maps based on multi-scale B-spline representations. Annals of Geophysics, 37(4), 699–717. https://doi.org/10.5194/angeo-37-699-2019
  16. ↵
    1. Goss, A.,
    2. Schmidt, M.,
    3. Erdogan, E., &
    4. Seitz, F.
    (2020). Global and regional high-resolution VTEC modelling using a two-step B-spline approach. Remote Sensing, 12(7), 1198. https://www.mdpi.com/2072-4292/12/7/1198
  17. ↵
    1. Habarulema, J. B.,
    2. McKinnell, L.-A.,
    3. Cilliers, P. J., &
    4. Opperman, B. D. L.
    (2009). Application of neural networks to South African GPS TEC modelling. Advances in Space Research, 43(11), 1711–1720. https://doi.org/10.1016/j.asr.2008.08.020
  18. ↵
    1. Hastie, T.,
    2. Tibshirani, R.,
    3. Friedman, J., & SpringerLink (Online service)
    . (2009). The Elements of Statistical Learning Data Mining, Inference, and Prediction (2nd edition). Springer-Verlag. http://iclibezp1.cc.ic.ac.uk/login?url=https://doi.org/10.1007/978-0-387-84858-7
  19. ↵
    1. Hernández-Pajares, M.,
    2. Juan, J. M.,
    3. Sanz, J.,
    4. Aragón-Àngel, À.,
    5. García-Rigo, A.,
    6. Salazar, D., &
    7. Escudero, M.
    (2011). The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques. Journal of Geodesy, 85(12), 887–907. https://doi.org/10.1007/s00190-011-0508-5
  20. ↵
    1. Hernández-Pajares, M.,
    2. Juan, J. M.,
    3. Sanz, J.,
    4. Orus, R.,
    5. Garcia-Rigo, A.,
    6. Feltens, J.,
    7. Komjathy, A.,
    8. Schaer, S. C., &
    9. Krankowski, A.
    (2009). The IGS VTEC maps: a reliable source of ionospheric information since 1998. Journal of Geodesy, 83(3), 263–275. https://doi.org/10.1007/s00190-008-0266-1
  21. ↵
    1. Hofmann-Wellenhof, B.,
    2. Lichtenegger, H., &
    3. Collins, J.
    (2001). Global positioning system theory and practice (5th revised edition). Springer Wien.
  22. ↵
    1. IERS conventions
    . (2010). (IERS Technical Note, Issue 36). Verlag des Bundesamts für Kartographie und Geodäsie.
  23. ↵
    1. Jee, G.,
    2. Lee, H.-B.,
    3. Kim, Y. H.,
    4. Chung, J.-K., and
    5. Cho, J.
    (2010), Assessment of GPS global ionosphere maps (GIM) by comparison between CODE GIM and TOPEX/Jason TEC data: Ionospheric perspective, J. Geophys. Res., 115, A10319, https://doi.org/10.1029/2010JA015432.
  24. ↵
    1. Jiang, H.,
    2. Wang, Z.,
    3. An, J.,
    4. Liu, J.,
    5. Wang, N., &
    6. Li, H.
    (2017). Influence of spatial gradients on ionospheric mapping using thin layer models. GPS Solutions, 22(1), 2. https://doi.org/10.1007/s10291-017-0671-0
  25. ↵
    1. Kaselimi, M.,
    2. Voulodimos, A.,
    3. Doulamis, N.,
    4. Doulamis, A., &
    5. Delikaraoglou, D.
    (2020). A causal long short-term memory sequence to sequence model for TEC prediction using GNSS observations. Remote Sensing, 12(9), 1354. https://www.mdpi.com/2072-4292/12/9/1354
  26. ↵
    1. Kim, M., &
    2. Kim, J.
    (2019). Extending the coverage area of regional ionosphere maps using a support vector machine algorithm. Annals of Geophysics, 37(1), 77–87. https://doi.org/10.5194/angeo-37-77-2019
  27. ↵
    1. Klobuchar, J. A.
    (1987). Ionospheric time-delay algorithm for single-frequency GPS users. IEEE Transactions on Aerospace and Electronic Systems, AES-23(3), 325–331. https://doi.org/10.1109/TAES.1987.310829
  28. ↵
    1. Leandro, R. F., &
    2. Santos, M. C.
    (2007). A neural network approach for regional vertical total electron content modelling. Studia Geophysica et Geodaetica, 51(2), 279–292. https://doi.org/10.1007/s11200-007-0015-6
  29. ↵
    1. LeNail, A.
    (2019). Nn-svg: publication-ready neural network architecture schematics. Journal of Open Source Software, 4(33), 747. https://doi.org/10.21105/joss.00747
  30. ↵
    1. Li, Z.,
    2. Wang, N.,
    3. Hernández-Pajares, M.,
    4. Yuan, Y.,
    5. Krankowski, A.,
    6. Liu, A.,
    7. Zha, J.,
    8. García-Rigo, A.,
    9. Roma-Dollase, D.,
    10. Yang, H.,
    11. Laurichesse, D., &
    12. Blot, A.
    (2020). IGS real-time service for global ionospheric total electron content modeling. Journal of Geodesy, 94(3), 32. https://doi.org/10.1007/s00190-020-01360-0
  31. ↵
    1. Liu, L.,
    2. Zou, S.,
    3. Yao, Y., &
    4. Wang, Z.
    (2020). Forecasting global ionospheric TEC using deep learning approach. Space Weather, 18(11), e2020SW002501. https://doi.org/10.1029/2020SW002501
  32. ↵
    1. Liu, Q.,
    2. Hernández-Pajares, M.,
    3. Lyu, H., &
    4. Goss, A.
    (2021). Influence of temporal resolution on the performance of global ionospheric maps. Journal of Geodesy, 95(3), 34. https://doi.org/10.1007/s00190-021-01483-y
  33. ↵
    1. Magnet, N.
    (2019). Giomo: A robust modelling approach of ionospheric delays for GNSS realtime positioning applications. [Dissertation, Vienna University of Technology]. https://doi.org/10.34726/hss.2019.21396
  34. ↵
    1. Mannucci, A. J.,
    2. Wilson, B. D.,
    3. Yuan, D. N.,
    4. Ho, C. H.,
    5. Lindqwister, U. J., &
    6. Runge, T. F.
    (1998). A global mapping technique for GPS-derived ionospheric total electron content measurements. Radio Science, 33(3), 565–582. https://doi.org/10.1029/97RS02707
    CrossRefWeb of Science
  35. ↵
    1. Motamedi, M.,
    2. Sakharnykh, N., &
    3. Kaldewey, T.
    (2021). A data-centric approach for training deep neural networks with less data. 35th Conference on Neural Information Processing Systems (NeurIPS 2021). https://doi.org/10.48550/arXiv.2110.03613
  36. ↵
    1. Natras, R.,
    2. Halilovic, D.,
    3. Mulić, M., &
    4. Schmidt, M.
    (2023). Mid-latitude ionosphere variability (2013–2016), and space weather impact on VTEC and precise point positioning. In: Ademović, N., Mujčić, E., Mulić, M., Kevrić, J., Akšamija, Z. (eds.) Advanced Technologies, Systems, and Applications VII. Lecture Notes in Networks and Systems (vol. 539). Springer. https://doi.org/10.1007/978-3-031-17697-5_37
  37. ↵
    1. Natras, R., &
    2. Schmidt, M.
    (2021). Machine learning model development for space weather forecasting in the ionosphere. In G. Cong & M. Ramanath, CIKM 2021 Workshops CEUR Workshop Proceedings, RWTH Aachen. http://ceur-ws.org/Vol-3052/short10.pdf.
  38. ↵
    1. Natras, R.,
    2. Soja, B., &
    3. Schmidt, M.
    (2022). Ensemble machine learning of Random Forest, AdaBoost and XGBoost for vertical total electron content forecasting. Remote Sensing, 14(15), 3547. https://doi.org/10.3390/rs14153547
  39. ↵
    1. Natras, R.,
    2. Soja, B., &
    3. Schmidt, M.
    (2022). Machine learning ensemble approach for ionosphere and space weather forecasting with uncertainty quantification. 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC). https://doi.org/10.23919/AT-AP-RASC54737.2022.9814334
  40. ↵
    1. Nava, B.,
    2. Coïsson, P., &
    3. Radicella, S. M.
    (2008). A new version of the NeQuick ionosphere electron density model. Journal of Atmospheric and Solar-Terrestrial Physics, 70(15), 1856–1862. https://doi.org/10.1016/j.jastp.2008.01.015
  41. ↵
    1. Okoh, D.,
    2. Owolabi, O.,
    3. Ekechukwu, C.,
    4. Folarin, O.,
    5. Arhiwo, G.,
    6. Agbo, J.,
    7. Bolaji, S., &
    8. Rabiu, B.
    (2016). A regional GNSS-VTEC model over Nigeria using neural networks: a novel approach. Geodesy and Geodynamics, 7(1), 19–31. https://doi.org/10.1016/j.geog.2016.03.003
  42. ↵
    1. Orus Perez, R.
    (2019). Using TensorFlow-based neural network to estimate GNSS single frequency ionospheric delay (IONONet). Advances in Space Research, 63(5), 1607–1618. https://doi.org/10.1016/j.asr.2018.11.011
  43. ↵
    1. Orus Perez, R.,
    2. Parro-Jimenez, J. M., &
    3. Prieto-Cerdeira, R.
    (2018). Status of NeQuick G after the solar maximum of cycle 24. Radio Science, 53(3), 257–268. https://doi.org/10.1002/2017RS006373
  44. ↵
    1. Orús, R.,
    2. Hernández-Pajares, M.,
    3. Juan, J. M.,
    4. Sanz, J., &
    5. García-Fernández, M.
    (2002). Performance of different TEC models to provide GPS ionospheric corrections. Journal of Atmospheric and Solar-Terrestrial Physics, 64(18), 2055–2062. https://doi.org/10.1016/S1364-6826(02)00224-9
  45. ↵
    1. Radicella, S. M., &
    2. Nava, B.
    (2020). Chapter 6: Empirical ionospheric models. In Materassi, M., Forte, B., Coster, A. J. & Skone, S (Eds.), The Dynamical Ionosphere. Elsevier. pp. 39–53 https://doi.org/10.1016/B978-0-12-814782-5.00006-6
  46. ↵
    1. Rajpurkar, P.,
    2. Park, A.,
    3. Irvin, J.,
    4. Chute, C.,
    5. Bereket, M.,
    6. Mastrodicasa, D.,
    7. Langlotz, C. P.,
    8. Lungren, M. P.,
    9. Ng, A. Y., &
    10. Patel, B. N.
    (2020). AppendiXNet: deep learning for diagnosis of appendicitis from a small dataset of CT exams using video pretraining. Scientific Reports, 10(1), 3958. https://doi.org/10.1038/s41598-020-61055-6
  47. ↵
    1. Ramchoun, H.,
    2. Idrissi, M. A. J.,
    3. Ghanou, Y., &
    4. Ettaouil, M.
    (2016). Multilayer perceptron: architecture optimization and training. International Journal of Interactive Multimedia and Artificial Intelligence, 4(1), 26–30. https://doi.org/10.9781/ijimai.2016.415
  48. ↵
    1. Ridley, A. J.,
    2. Deng, Y., &
    3. Tóth, G.
    (2006). The global ionosphere–thermosphere model. Journal of Atmospheric and Solar–Terrestrial Physics, 68(8), 839–864. https://doi.org/10.1016/j.jastp.2006.01.008
  49. ↵
    1. Schaer, S.
    (1999). Mapping and predicting the Earth’s ionosphere using the global positioning system. [Dissertation, The University of Bern].
  50. ↵
    1. Schunk, R. W.,
    2. Scherliess, L.,
    3. Sojka, J. J.,
    4. Thompson, D. C.,
    5. Anderson, D. N.,
    6. Codrescu, M.,
    7. Minter, C.,
    8. Fuller-Rowell, T. J.,
    9. Heelis, R. A.,
    10. Hairston, M., &
    11. Howe, B. M.
    (2004). Global assimilation of ionospheric measurements (GAIM). Radio Science, 39(1), RS1S02. https://doi.org/10.1029/2002RS002794
  51. ↵
    1. Sharma, S.,
    2. Sharma, S., &
    3. Athaiya, A.
    (2020). Activation functions in neural networks. International Journal of Engineering Applied Sciences and Technology, 4(12), 310–316. https://ijeast.com/papers/310-316,Tesma412,IJEAST.pdf
  52. ↵
    1. Shi, C.,
    2. Zhang, T.,
    3. Wang, C.,
    4. Wang, Z., &
    5. Fan, L.
    (2019). Comparison of IRI-2016 model with IGS VTEC maps during low and high solar activity period. Results in Physics, 12, 555–561. https://doi.org/10.1016/j.rinp.2018.12.022
  53. ↵
    1. Sugiura, M.
    (1964). Hourly values of equatorial Dst for the IGY. Annals of the International Geophysical Year, 35, 9–45.
  54. ↵
    1. Wang, N.,
    2. Yuan, Y.,
    3. Li, Z., &
    4. Huo, X.
    (2013). Impact of ionospheric correction on single-frequency GNSS positioning. In Sun, J., Jiao, W., Wu, H. & Shi, C. China Satellite Navigation Conference (CSNC) 2013 Proceedings Berlin, Heidelberg.
  55. ↵
    1. Wang, N.,
    2. Yuan, Y.,
    3. Li, Z.,
    4. Li, Y.,
    5. Huo, X., &
    6. Li, M.
    (2017). An examination of the Galileo NeQuick model: comparison with GPS and JASON TEC. GPS Solutions, 21(2), 605–615. https://doi.org/10.1007/s10291-016-0553-x
  56. ↵
    1. Wild, U.
    (1994). Ionosphere and geodetic satellite systems permanent GPS tracking data for modelling and monitoring. [Dissertation, The University of Bern].
  57. ↵
    1. Zhang, Z.,
    2. Pan, S.,
    3. Gao, C.,
    4. Zhao, T., &
    5. Gao, W.
    (2019). Support vector machine for regional ionospheric delay modeling. Sensors, 19(13), 2947. https://doi.org/10.3390/s19132947.
  58. ↵
    1. Zhao, T.,
    2. Pan, S.,
    3. Gao, W.,
    4. Qing, Z.,
    5. Yang, X., &
    6. Wang, J.
    (2021). Extreme learning machine-based spherical harmonic for fast ionospheric delay modeling. Journal of Atmospheric and SolarTerrestrial Physics, 216, 105590. https://doi.org/10.1016/j.jastp.2021.105590
  59. ↵
    1. Zheng, A., &
    2. Casari, A.
    (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly Media, Inc.
PreviousNext
Back to top

In this issue

NAVIGATION: Journal of the Institute of Navigation: 70 (3)
NAVIGATION: Journal of the Institute of Navigation
Vol. 70, Issue 3
Fall 2023
  • Table of Contents
  • Index by author
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on NAVIGATION: Journal of the Institute of Navigation.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Regional Ionosphere Delay Models Based on CORS Data and Machine Learning
(Your Name) has sent you a message from NAVIGATION: Journal of the Institute of Navigation
(Your Name) thought you would like to see the NAVIGATION: Journal of the Institute of Navigation web site.
Citation Tools
Regional Ionosphere Delay Models Based on CORS Data and Machine Learning
Randa Natras, Andreas Goss, Dzana Halilovic, Nina Magnet, Medzida Mulic, Michael Schmidt,, Robert Weber
NAVIGATION: Journal of the Institute of Navigation Sep 2023, 70 (3) navi.577; DOI: 10.33012/navi.577

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Regional Ionosphere Delay Models Based on CORS Data and Machine Learning
Randa Natras, Andreas Goss, Dzana Halilovic, Nina Magnet, Medzida Mulic, Michael Schmidt,, Robert Weber
NAVIGATION: Journal of the Institute of Navigation Sep 2023, 70 (3) navi.577; DOI: 10.33012/navi.577
Reddit logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • 1 INTRODUCTION
    • 2 METHODOLOGY
    • 3 RESULTS
    • 4 DISCUSSION AND CONCLUSIONS
    • HOW TO CITE THIS ARTICLE
    • FUNDING
    • CONFLICT OF INTEREST / COMPETING INTERESTS
    • AUTHORS’ CONTRIBUTION
    • ACKNOWLEDGMENTS
    • REFERENCES
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF

Related Articles

  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Multi-layered Multi-Constellation Global Navigation Satellite System Interference Mitigation
  • Instantaneous Sub-meter Level Precise Point Positioning of Low-Cost Smartphones
  • SBAS Protection Levels with Gauss-Markov K-Factors for Any Integrity Target
Show more Original Article

Similar Articles

Keywords

  • artificial neural network
  • ionosphere delay modeling
  • machine learning
  • regional ionosphere model
  • single-frequency positioning
  • vertical total electron content

Unless otherwise noted, NAVIGATION content is licensed under a Creative Commons CC BY 4.0 License.

© 2023 The Institute of Navigation, Inc.

Powered by HighWire