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GPS Solutions

, 22:107 | Cite as

A modified three-dimensional ionospheric tomography algorithm with side rays

  • Yibin Yao
  • Changzhi Zhai
  • Jian Kong
  • Qingzhi Zhao
  • Cunjie Zhao
Original Article
  • 51 Downloads

Abstract

The three-dimensional ionospheric tomography (3DCIT) algorithm based on Global Navigation Satellite System (GNSS) observations have been developed into an effective tool for ionospheric monitoring in recent years. However, because the rays that come into or come out from the side of the inversion region cannot be used, the distribution of the rays in the edge and bottom part of the inversion region is scarce and the electron density cannot be effectively improved in the inversion process. We present a three-dimensional tomography algorithm with side rays (3DCIT-SR) applying the side rays to the inversion. The partial slant total electron content (STEC) of side rays in the inversion region is obtained based on the NeQuick2 model and GNSS-STEC. The simulation experiment results show that the algorithm can effectively improve the distribution of GNSS rays in the inversion region. Meanwhile, the iteration accuracy has also been significantly improved. After the same number of iterations, the iterative results of 3DCIT-SR are closer to the truth than 3DCIT, in particular, the inversion of the edge regions is improved noticeably. The GNSS data of the International GNSS Service (IGS) stations in Europe are used to perform real data experiments, and the inversion results show that the electron density profiles of 3DCIT-SR are closer to the ionosonde measurements. The accuracy improvement of 3DCIT-SR is up to 56.3% while the improvement is more obvious during the magnetic storm compared to the case of a calm ionospheric state .

Keywords

Three-dimensional ionospheric tomography GNSS Side rays Inversion 

Notes

Acknowledgements

The authors would like to thank the International Global Navigation Satellite System Service (IGS) for the data used in this work. The authors also thank the Global Ionosphere Radio Observatory for the ionosonde data. The ionosonde (PQ052) data were downloaded from ftp://ftp.ngdc.noaa.gov/ionosonde/data/.

References

  1. Austen JR, Franke SJ, Liu CH, Yeh KC (1986) Application of computerized tomography techniques to ionospheric research. In: Proceedings of the international beacon satellite symposium on radio beacon contribution to the study of ionization and dynamics of the ionosphere and to corrections to geodesy and technical workshop, University of Oulu, Finland, pp 25–35. http://adsabs.harvard.edu/abs/1986ibs..symp...2A
  2. Austen JR, Franke SJ, Liu CH (1988) Ionospheric imaging using computerized tomography. Radio Sci 23(3):299–307CrossRefGoogle Scholar
  3. Burrell AG, Bonito NA, Carrano CS (2009) Total electron content processing from GPS observations to facilitate ionospheric modeling. GPS Solut 13(2):83–95CrossRefGoogle Scholar
  4. Bust GS, Coco D, Makela JJ (2000) Combined ionospheric campaign 1: ionospheric tomography and GPS total electron count (TEC) depletions. Geophys Res Lett 27(18):2849–2852CrossRefGoogle Scholar
  5. Chen CH, Saito A, Lin CH, Yamamoto M, Suzuki S, Seemala GK (2016) Medium-scale traveling ionospheric disturbances by three-dimensional ionospheric GPS tomography. Earth Planets Space 68(32):1–9Google Scholar
  6. Das SK, Shukla AK (2011) Two-dimensional ionospheric tomography over the low-latitude Indian region: an intercomparison of ART and MART algorithms. Radio Sci.  https://doi.org/10.1029/2010RS004350 Google Scholar
  7. Ezquer RG, Scidá LA, Orué YM, Lescano GE, Alazo-Cuartas K, Cabrera MA, Radicella SM (2017) NeQuick 2 total electron content predictions for middle latitudes of North American region during a deep solar minimum. J Atmos Sol Terr Phy 154(2):55–66CrossRefGoogle Scholar
  8. Farzaneh S, Forootan E (2018) Reconstructing regional ionospheric electron density: a combined spherical slepian function and empirical orthogonal function approach. Surv Geophys 39(2):289–309.  https://doi.org/10.1007/s10712-017-9446-y CrossRefGoogle Scholar
  9. Fehmers GC, Kamp L, Sluijter FW, Spoelstra T (1998) A model-independent algorithm for ionospheric tomography: 2. Experimental results. Radio Sci 33(1):165–173CrossRefGoogle Scholar
  10. Gerzen T, Minkwitz D (2016) Simultaneous multiplicative column-normalized method (SMART) for 3-D ionosphere tomography in comparison to other algebraic methods. Ann Geophys Germ 34(1):97–115.  https://doi.org/10.5194/angeo-34-97-2016 CrossRefGoogle Scholar
  11. Han D, Kim D, Kee C (2018) Improving performance of GPS satellite DCB estimation for regional GPS networks using long-term stability. GPS Solut 22(1):13CrossRefGoogle Scholar
  12. Hansen AJ, Walter T, Enge P (1997) Ionospheric correction using tomography. In: Proc. ION GPS-97, Institute of Navigation, Kansas City, MO, USA, September 16–19, pp 249–260Google Scholar
  13. Hernández-Pajares M, Juan JM, Sanz J, Sol Eacute JG (1998) Global observation of the ionospheric electronic response to solar events using ground and LEO GPS data. J Geophys Res-Space 103(A9):20789–20796CrossRefGoogle Scholar
  14. Hernández-Pajares M, Juan JM, Sanz J, Colombo OL (2000) Application of ionospheric tomography to real-time GPS carrier-phase ambiguities resolution, at scales of 400–1000 km and with high geomagnetic activity. Geophys Res Lett 27(13):2009–2012CrossRefGoogle Scholar
  15. Hirooka S, Hattori K (2016) Validation of Ionospheric Tomography Using Residual Minimization Training Neural Network. Electr Commun Jpn 99(4):50–57CrossRefGoogle Scholar
  16. Hirooka S, Hattori K, Takeda T (2011a) Numerical validations of neural-network-based ionospheric tomography for disturbed ionospheric conditions and sparse data. Radio Sci 46(5):RS0F05.  https://doi.org/10.1029/2011RS004760 CrossRefGoogle Scholar
  17. Hirooka S, Hattori K, Nishihashi M, Takeda T (2011b) Neural network based tomographic approach to detect earthquake-related ionospheric anomalies. Nat Hazard Earth Sys 11(8):2341–2353CrossRefGoogle Scholar
  18. Hobiger T, Kondo T, Koyama Y (2008) Constrained simultaneous algebraic reconstruction technique (C-SART)—a new and simple algorithm applied to ionospheric tomography. Earth Planets Space 60(7):727–735CrossRefGoogle Scholar
  19. Howe BM, Runciman K, Secan JA (1998) Tomography of the ionosphere: four-dimensional simulations. Radio Sci 33(1):109–128CrossRefGoogle Scholar
  20. Huang CR, Liu CH, Yeh KC, Lin KH, Tsai WH, Yeh HC, Liu JY (1999) A study of tomographically reconstructed ionospheric images during a solar eclipse. J Geophys Res Space 104(A1):79–94CrossRefGoogle Scholar
  21. Jin S, Li D (2018) 3-D ionospheric tomography from dense GNSS observations based on an improved two-step iterative algorithm. Adv Space Res 62(4):809–820CrossRefGoogle Scholar
  22. Jin R, Jin S, Feng G (2012) M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases. GPS Solut 16(4):541–548.  https://doi.org/10.1007/s10291-012-0279-3 CrossRefGoogle Scholar
  23. Kunitsyn VE, Andreeva ES, Razinkov OG (1997) Possibilities of the near-space environment radio tomography. Radio Sci 32(5):1953–1963CrossRefGoogle Scholar
  24. Lee JK, Kamalabadi F, Makela JJ (2007) Localized three-dimensional ionospheric tomography with GPS ground receiver measurements. Radio Sci 42(04):1–15CrossRefGoogle Scholar
  25. Ma XF, Maruyama T, Ma G, Takeda T (2005) Three-dimensional ionospheric tomography using observation data of GPS ground receivers and ionosonde by neural network. J Geophys Res Space 110(A5):A5308.  https://doi.org/10.1029/2004JA010797 CrossRefGoogle Scholar
  26. Macalalad FV, Jr JH, Apostol JV, Preston FW (1993) Experimental ionospheric tomography with ionosonde input and EISCAT verification. Ann Geophys Ger 11(11–12):1064–1074Google Scholar
  27. Markkanen M, Lehtinen M, Nygrén T, Pirttilä J, Henelius P, Vilenius E, Tereshchenko ED, Khudukon BZ (1995) Bayesian approach to satellite radiotomography with applications in the Scandinavian sector. ISME J 8(11):2280–2289Google Scholar
  28. Mitchell CN, Spencer PSJ (2003) A three-dimensional time-dependent algorithm for ionospheric imaging using GPS. Ann Geophys Italy 46(4):687–696Google Scholar
  29. Nava B, Coisson P, Radicella SM (2008) A new version of the NeQuick ionosphere electron density model. J Atmos Solar Terr Phys 70(15):1856–1862CrossRefGoogle Scholar
  30. Nava B, Radicella SM, Azpilicueta F (2011) Data ingestion into NeQuick 2. Radio Sci, 46(6):RS0D17.  https://doi.org/10.1029/2010RS004635, 2011CrossRefGoogle Scholar
  31. Nigussie M, Radicella SM, Damtie B, Nava B, Yizengaw E, Groves K (2013) Validation of the NeQuick 2 and IRI-2007 models in East-African equatorial region. J Atmos Sol Terr Phy 102(9):26–33CrossRefGoogle Scholar
  32. Norberg J, Roininen L, Vierinen J, Amm O, Mckay-Bukowski D, Lehtinen M (2015) Ionospheric tomography in Bayesian framework with Gaussian Markov random field priors. Radio Sci 50(2):138–152CrossRefGoogle Scholar
  33. Nygrén T, Markkanen M, Lehtinen M, Tereshchenko ED, Khudukon BZ (1997) Stochastic inversion in ionospheric radiotomography. Radio Sci 32(6):2359–2372.  https://doi.org/10.1029/97RS02915 CrossRefGoogle Scholar
  34. Oladipo OA, Schüler T (2012) GNSS single frequency ionospheric range delay corrections: NeQuick data ingestion technique. Adv Space Res 50(9):1204–1212CrossRefGoogle Scholar
  35. Prol FS, Camargo PO, Muella MTAH (2017) Numerical simulations to assess ART and MART performance for ionospheric tomography of Chapman profiles. An Acad Bras Cienc 89(3):1531–1542.  https://doi.org/10.1590/0001-3765201720170116 CrossRefGoogle Scholar
  36. Pryse SE, Kersley L, Rice DL, Russell CD, Walker IK (1993) Tomographic imaging of the ionospheric mid-latitude trough. Ann Geophys 11(3):144–149Google Scholar
  37. Pryse SE, Kersley L, Williams MJ, Walker IK, Willson CA (1997) Tomographic imaging of the polar-cap ionosphere over svalbard. J Atmos Sol Terr Phy 59(15):1953–1959CrossRefGoogle Scholar
  38. Raymond TD, Franke SJ, Yeh KC (1994) Ionospheric tomography: its limitations and reconstruction methods. J Atmospheric Sol Terr Phys 56(5):637–653CrossRefGoogle Scholar
  39. Raymund TD, Austen JR, Franke SJ, Liu CH, Klobuchar JA, Stalker J (1990) Application of computerized tomography to the investigation of ionospheric structures. Radio Sci 25(05):771–789CrossRefGoogle Scholar
  40. Rideout W, Coster A (2006) Automated GPS processing for global total electron content data. GPS Solut 10(3):219–228CrossRefGoogle Scholar
  41. Rius A, Ruffini G, Cucurull L (1997) Improving the vertical resolution of ionospheric tomography with GPS occultations. Geophys Res Lett 24(18):2291–2294CrossRefGoogle Scholar
  42. Saito S, Suzuki S, Yamamoto M, Saito A, Chen CH (2017) Real-time ionosphere monitoring by three-dimensional tomography over Japan. Navig J Inst Navig 64(4):495–504CrossRefGoogle Scholar
  43. Schüler T, Oladipo OA (2014) Single-frequency single-site VTEC retrieval using the NeQuick2 ray tracer for obliquity factor determination. GPS Solut 18(1):115–122CrossRefGoogle Scholar
  44. Seemala GK, Yamamoto M, Saito A, Chen CH (2014) Three-dimensional GPS ionospheric tomography over Japan using constrained least squares. J Geophys Res Space 119(4):3044–3052CrossRefGoogle Scholar
  45. Ssessanga N, Kim YH, Kim E (2015) Vertical structure of medium-scale traveling ionospheric disturbances. Geophys Res Lett 42(21):9156–9165CrossRefGoogle Scholar
  46. Themens DR, Jayachandran PT, Langley RB, MacDougall JW, Nicolls MJ (2013) Determining receiver biases in GPS-derived total electron content in the auroral oval and polar cap region using ionosonde measurements. GPS Solut 17(3):357–369CrossRefGoogle Scholar
  47. Wang S, Huang S, Xiang J, Fang H, Feng J, Wang Y (2016) Three-dimensional ionospheric tomography reconstruction using the model function approach in Tikhonov regularization. J Geophys Res Space 121(12):104–112.  https://doi.org/10.1002/2016JA023487 (115)Google Scholar
  48. Wen D, Yuan Y, Ou J, Huo X, Zhang K (2007) Three-dimensional ionospheric tomography by an improved algebraic reconstruction technique. GPS Solut 11(4):251–258CrossRefGoogle Scholar
  49. Wen D, Yuan Y, Ou J, Zhang K, Liu K (2008) A hybrid reconstruction algorithm for 3-D ionospheric tomography. IEEE Trans Geosci Remote Sens 46(6):1733–1739CrossRefGoogle Scholar
  50. Wen D, Wang Y, Norman R (2012) A new two-step algorithm for ionospheric tomography solution. GPS Solut 16(1):89–94.  https://doi.org/10.1007/s10291-011-0211-2 CrossRefGoogle Scholar
  51. Yao Y, Kong J, Tang J (2015) A new ionosphere tomography algorithm with two-grid virtual observations constraints and three-dimensional velocity profile. IEEE Trans Geosci Remote 53(5):2373–2383.  https://doi.org/10.1109/TGRS.2014.2359762 CrossRefGoogle Scholar
  52. Yao YB, Zhao QZ, Zhang B (2016) A method to improve the utilization of GNSS observation for water vapor tomography. Ann Geophys Ger 34(1):143–152.  https://doi.org/10.5194/angeo-34-143-2016 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Geodesy and GeomaticsWuhan UniversityWuhanChina
  2. 2.Chinese Antarctic Center of Surveying and MappingWuhan UniversityWuhanChina
  3. 3.College of GeomaticsXi’an University of Science and TechnologyXi’anChina

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