Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Czasopismo
2022 | Vol. 70, no 1 | 429--443
Tytuł artykułu

Influence of input parameters for prediction of GPS and IRNSS TEC by using OKRSM at Hyderabad stations during solar fare event

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The solar fare is a threat to the Global Positioning Satellite System (GPS). In this paper, the forecasting capability of Ordinary Kriging-based Response Surface Model (OKRSM) is examined during the X2.2 and X9.3 solar fare that occurred on 6.9.2017. Additionally, its effect on positional accuracy of GPS and IRNSS (Indian Regional Navigation Satellite System) is also evaluated. The GPS and IRNSS VTEC (Vertical Total Electron Content) data are taken from the Hyderabad IGS network station and IRNSS receiver installed at Osmania University, Hyderabad station (17° 24′ 28.07″ N, 78° 31′ 4.26″ E), respectively. The VTEC data of GPS and IRNSS are forecasted from 4th September 2017 to 6th September 2017 by using the previous 6 days of input parameters, GPS and IRNSS TEC data. The parameters like SSN, Kp, Ap, F10.7, ScalarB, Vector B Mag, RMS Mag, RMS Field Vec and Dst were used as inputs for constructing the response surface model. Based on the prediction during the solar fare, it is noted that the model, which was built by using the parameters like ScalarB, Vector B Mag, RMS Field Vec and Dst, gives better prediction results and less range error when compared to other cases used for prediction of TEC. To validate the constructed model, the forecasted TEC of GPS and IRNSS during solar fare is compared with IRI 2016 and IRI PLAS 2017 models. Based on the comparison results, it is found that the developed model showing good agreement with IRNSS data rather than GPS data. The obtained results also show that the IRNSS constellation provides better service in the Indian region when compared to GPS and other models.
Słowa kluczowe
PL
OKRSM   IRNSS   GPS   X9.3   błąd zakresu  
EN
OKRSM   IRNSS   GPS   X9.3   range error  
Wydawca

Czasopismo
Rocznik
Strony
429--443
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
  • Department of ECE, PSNA College of Engineering and Technology, Dindigul, India
autor
  • Department of Aerospace Engineering, ACS College of Engineering, Bangalore, India, vsmprm@gmail.com
  • Department of Aerospace Engineering, ACS College of Engineering, Bangalore, India
  • Department of ECE, Osmania University, Hyderabad, India
Bibliografia
  • 1. Ansari K, Panda SK, Corumluoglu O (2018) Mathematical modelling of ionospheric TEC from Turkish permanent GNSS Network (TPGN) observables during 2009–2017 and predictability of NeQuick and Kriging models. Astrophys Space Sci 363:42. https://doi.org/10.1007/s10509-018-3261-x
  • 2. Desai MV, Shah SN (2019) Estimation of ionospheric delay of NavIC/IRNSS signals using the Taylor Series Expansion. J Space Weather Space Clim 9:A23. https://doi.org/10.1051/swsc/2019023
  • 3. Desai MV, Shah SN (2020) An observational review on influence of intense geomagnetic storm on positional accuracy of NavIC system. IETE Tech Rev 37(3):281–295. https://doi.org/10.1080/02564602.2019.1599739
  • 4. Devireddy K, Narsetty S, Ramavath AK, Perumalla NK (2020) Validation of the IRI-2016 model with Indian NavIC data for future navigation applications. IET Radar Sonar Navig 15(1):37–50. https://doi.org/10.1049/rsn2.12013
  • 5. Erken F, Karatay S, Çınar A (2019) Spatio-temporal prediction of ionospheric total electron content using an adaptive data fusion technique. Geomagn Aeron. https://doi.org/10.1134/S001679321908005X
  • 6. Hernández-Pajares M, Juan JM, Sanz J et al (2011) The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques. J Geod 85:887–907. https://doi.org/10.1007/s00190-011-0508-5
  • 7. Kumar S, Singh AK (2011) Storm time response of GPS-derived total electron content (TEC) during low solar active period at Indian low latitude station Varanasi. Astrophys Space Sci 331:447–458. https://doi.org/10.1007/s10509-010-0459-y
  • 8. Kumar S, Priyadarshi S, Gopi Krishna S, Singh AK (2012) GPS-TEC variations during low solar activity period (2007–2009) at Indian low latitude stations. Astrophys Space Sci 339:165–178. https://doi.org/10.1007/s10509-011-0973-6
  • 9. Kunitsyn VE, Padokhin AM, Kurbatov GA, Yasyukevich YuV, Morozov YuV (2016) Ionospheric TEC estimation with the signals of various geostationary navigational satellites. GPS Solut 20:877–884. https://doi.org/10.1007/s10291-015-0500-2
  • 10. Mallika IL, Ratnam DV, Raman S, Sivavaraprasad G et al (2020a) Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations. Astrophys Space Sci 365:124. https://doi.org/10.1007/s10509-020-03821-6
  • 11. Mallika IL, Ratnam DV, Raman S, Sivavaraprasad G (2020b) Machine learning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations. Acta Astronaut 173:221–231. https://doi.org/10.1016/j.actaastro.2020.04.048
  • 12. Mukesh R, Karthikeyan V, Soma P, Sindhu P (2019) Analysis of signal strength, satellite visibility, position accuracy and ionospheric TEC estimation of IRNSS. Astrophys Space Sci 364:196. https://doi.org/10.1007/s10509-019-3676-z
  • 13. Mukesh R, Karthikeyan V, Soma P, Sindhu P (2020a) Ordinary kriging-and Cokriging-based surrogate model for ionospheric TEC prediction using NaviC/GPS data. Acta Geophys 68:1529–1547. https://doi.org/10.1007/s11600-020-00473-6
  • 14. Mukesh R, Karthikeyan V, Soma P, Sindhu P (2020b) Forecasting of ionospheric TEC for different latitudes, seasons and solar activity conditions based on OKSM. Astrophys Space Sci 365:13. https://doi.org/10.1007/s10509-020-3730-x
  • 15. Pajares M, Aragon-Angel A (2014) Use of GNSS derived ionospheric information to detect and measure Solar Flares. Física de la Tierra 26
  • 16. Rao SS, Chakraborty M, Kumar S, Singh AK (2019) Low-latitude ionospheric response from GPS, IRI and TIE-GCM TEC to Solar Cycle 24. Astrophys Space Sci 364:216. https://doi.org/10.1007/s10509-019-3701-2
  • 17. Reddybattula KD, Panda SK (2019) Performance analysis of quiet and disturbed time ionospheric TEC responses from GPS-based observations, IGS-GIM, IRI-2016 and SPIM/IRI-Plas 2017 models over the low latitude Indian region. Adv Space Res 64(10):2026–2045. https://doi.org/10.1016/j.asr.2019.03.034
  • 18. Sayin I, Arikan F, Arikan O (2008) Regional TEC mapping with Random Field Priors and Kriging. Radio Sci. https://doi.org/10.1029/2007RS003786
  • 19. Sivavaraprasad G, Deepika VS, Sreenivasa Rao D, Ravi Kumar M, Sridhar M (2020) Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station. Geod Geodyn 11(3):192–201. https://doi.org/10.1016/j.geog.2019.11.002
  • 20. Venkata Ratnam D, Vindhya G, Dabbakuti JRK (2017) Ionospheric forecasting model using fuzzy logic-based gradient descent method. Geod Geodyn 8(5):305–310. https://doi.org/10.1016/j.geog.2017.05.003
  • 21. Xiong P, Zhai D, Long C, Zhou H, Zhang X, Shen X (2021) Long short-term memory neural network for ionospheric total electron content forecasting over China. Space Weather. https://doi.org/10.1029/2020SW002706
  • 22. Yasyukevich Y, Yasyukevich A, Ratovsky K, klimenko M, Klimenko V, Chirik N (2018) Winter anomaly in NmF2 and TEC: When and where it can occur. J Space Weather Space Clim 8:A45. https://doi.org/10.1051/swsc/2018036
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-d2742d79-45af-49e4-b2e1-88af0cb4f0d3
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.