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Optimal 3D deformation measuring in inclined geosynchronous orbit SAR differential interferometry

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Abstract

Three dimensional (3D) deformation can be obtained by using differential interferometric synthetic aperture radar (D-InSAR) technique with the cross-heading tracks data of low earth orbit (LEO) SAR. However, this method has drawbacks of the low temporal sampling rate and the limited area and accuracy for 3D defor- mation retrieval. To address the aforementioned problems, by virtue of a geosynchronous (GEO) SAR platform, this paper firstly demonstrates the expressions of 3D deformation and the corresponding errors in GEO SAR multi-angle processing. An optimal multi-angle data selection method based on minimizing position dilution of precision (PDOP) is proposed to obtain a good 3D deformation retrieval accuracy. Moreover, neural network is utilized for analyzing the accuracy of the retrieved 3D deformation under different orbit configurations and geo-locations. Finally, the proposed methods and the theoretical analysis are verified by simulation experiments. A 3D deformation retrieval accuracy of the order of centimeter-level or even millimeter-level can be obtained by using the selected optimal multi-angle data.

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References

  1. 1

    Massonnet D, Rossi M, Carmona C, et al. The displacement field of the Landers earthquake mapped by radar interferometry. Nature, 1993, 364: 138–142

  2. 2

    Ferretti A, Monti Guarnieri A, Prati C, et al. InSAR Principles. Netherlands: ESA Publications,2007. B-11-CB-55

  3. 3

    Huang R Q, Fan X M. The landslide story. Nature Geosci, 2013, 6: 325–326

  4. 4

    Hu J, Li Z W, Ding X L, et al. 3D coseismic Displacement of 2010 Darfield, New Zealand earthquake estimated from multi-aperture InSAR and D-InSAR measurements. J Geodesy, 2012, 86: 1029–1041

  5. 5

    Wright T J, Parsons B E, Zhong L. Toward mapping surface deformation in three dimensions using InSAR. Geophys Res Lett, 2004, 31: L01607

  6. 6

    Ansari H, De Zan F, Parizzi A, et al. Measuring 3D surface motion with future SAR systems based on reflector antennae. IEEE Geosci Remote Sens Lett, 2016, 13: 272–276

  7. 7

    Gudmundsson S, Sigmundsson F, Carstensen J M. Three-dimensional surface motion maps estimated from combined interferometric synthetic aperture radar and GPS data. J Geophys Res, 2002, 107: ETG 13-1C-ETG 13-14

  8. 8

    Tomiyasu K. Synthetic aperture radar in geosynchronous orbit. In: Proceedings of IEEE Antennas and Propagation Society International Symposium, College Park, 1978. 42–45

  9. 9

    Prati C, Rocca F, Giancola D, et al. Passive geosynchronous SAR system reusing backscattered digital audio broad- casting signals. IEEE Trans Geosci Remote Sens, 1998, 36: 1973–1976

  10. 10

    Bruno D, Hobbs S E, Ottavianelli G. Geosynchronous synthetic aperture radar: Concept design, properties and possible applications. Acta Astronaut, 2006, 59: 149–156

  11. 11

    Monti Guarnieri A, Bombaci O, Catalano T F, et al. ARGOS: a fractioned geosynchronous SAR. Acta Astronaut, in press

  12. 12

    Monti Guarnieri A, Broquetas A, Recchia A, et al. Advanced radar geosynchronous observation system: ARGOS. IEEE Geosci Remote Sens Lett, 2015, 12: 1406–1410

  13. 13

    Hobbs S, Mitchell C, Forte B, et al. System design for geosynchronous synthetic aperture radar missions. IEEE Trans Geosci Remote Sens, 2014, 52: 7750–7763

  14. 14

    Hu C, Long T, Zeng T, et al. The accurate focusing and resolution analysis method in geosynchronous SAR. IEEE Trans Geosci Remote Sens, 2011, 49: 3548–3563

  15. 15

    Bruno D, Hobbs S E. Radar imaging from geosynchronous orbit: temporal decorrelation aspects. IEEE Trans Geosci Remote Sens, 2010, 48: 2924–2929

  16. 16

    Ding Z G, Yin W, Zeng T, et al. Radar parameter design for geosynchronous SAR in squint mode and elliptical orbit. IEEE J Sel Top Appl Earth Observ Remote Sens, 2016, 9: 2720–2732

  17. 17

    Dong X C, Hu C, Tian W M, et al. Feasibility study of inclined geosynchronous SAR focusing using Beidou IGSO signals. SCI China Inf Sci, 2016, 59: 129302

  18. 18

    Recchia A, Monti Guarnieri A, Broquetas A, et al. Impact of scene decorrelation on geosynchronous SAR data focusing. IEEE Trans Geosci Remote Sens, 2016, 54: 1635–1646

  19. 19

    Ding Z G, Shu B Z, Yin W, et al. A modified frequency domain algorithm based on optimal azimuth quadratic factor compensation for geosynchronous SAR imaging. IEEE J Sel Top Appl Earth Observ Remote Sens, 2015, 9: 1119–1131

  20. 20

    Ruiz Rodon J, Broquetas A, Monti Guarnieri A, et al. Geosynchronous SAR focusing with atmospheric phase screen retrieval and compensation. IEEE Trans Geosci Remote Sens, 2013, 51: 4397–4404

  21. 21

    Li Y H, Hu C, Dong X C, et al. Impacts of ionospheric scintillation on geosynchronous SAR focusing: preliminary experiments and analysis. Sci China Inf Sci, 2015, 58: 109301

  22. 22

    Hu C, Li Y H, Dong X C, et al. Performance analysis of L-band geosynchronous SAR imaging in the presence of ionospheric scintillation. IEEE Trans Geosci Remote Sens, 2017, 55: 159–172

  23. 23

    Hu C, Li Y H, Dong X C, et al. Optimal data acquisition and height retrieval in repeat-track geosynchronous SAR interferometry. Remote sens, 2015, 7: 13367–13389

  24. 24

    NASA, JPL. Global Earthquake Satellite System, a 20 year plan to enable earthquake prediction. 2003

  25. 25

    Kou L L, Wang X Q, Xiang M S, et al. Interferometric estimation of three-dimensional surface deformation using geosynchronous circular SAR. IEEE Trans Aerosp Electron Syst, 2012, 48: 1619–1635

  26. 26

    Hobbs S. GeoSAR: Summary of the Group Design Project MSc in Astronautics and Space Engineering 2005/06 Cranfield University. College of Aeronautics Report 0509. Cranfield University.2006

  27. 27

    Hu C, Li X R, Long T, et al. GEO SAR interferometry: theory and feasibility study. In: Proceedings of IET International Radar conference, Xi’an, 2013. 1–5

  28. 28

    Monti Guarnieri A, Tebaldini S, Rocca F, et al. GEMINI: geosynchronous SAR for Earth monitoring by interferometry and imaging. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012. 210–213

  29. 29

    Ruiz-Rodon J, Broquetas A, Makhoul E, et al. Nearly zero inclination geosynchronous SAR mission analysis with long integration time for Earth observation. IEEE Trans Geosci Remote Sens, 2014, 52: 6379–6391

  30. 30

    Hu C, Li Y H, Dong X C, et al. Impacts of temporal-spatial variant background ionosphere on repeat-track GEO D-InSAR system. Remote sens, 2016, 8: 916

  31. 31

    Dana P H. Global positioning system overview. Department of Geography, University of Texas at Austin.2000. http://www.colorado.edu/geography/gcraft/notes/gps/gps f.html

  32. 32

    Stramondo S, Del Frate F, Picchiani M, et al. Seismic source quantitative parameters retrieval from InSAR data and neural networks. IEEE Trans Geosci Remote Sens, 2011, 49: 96–104

  33. 33

    Lippmann R. An introduction to computing with neural nets. IEEE Assp Mag, 1987, 4: 4–22

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61427802, 61471038, 61501032), Chang Jiang Scholars Program (Grant No. T2012122), 111 Project of China (Grant No. B14010), and China Scholarship Council (CSC).

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Correspondence to Yuanhao Li.

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Hu, C., Li, Y., Dong, X. et al. Optimal 3D deformation measuring in inclined geosynchronous orbit SAR differential interferometry. Sci. China Inf. Sci. 60, 060303 (2017). https://doi.org/10.1007/s11432-016-9083-4

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Keywords

  • geosynchronous synthetic aperture radar (GEO SAR)
  • three dimensional (3D) deformation measurement
  • multi-angle processing
  • differential SAR (D-InSAR)
  • neural network