收稿：2018-01-23，
录用：2018-4-24，
纸质出版：2019-05
移动端阅览
DDM波形是GNSS-R技术用于反演地球物理参量的基本观测量，其仿真结果的可靠性直接影响GNSS-R的理论研究及项目工程参数设计。利用GREEPS仿真软件分析了时延间隔与多普勒间隔对DDM波形仿真结果可靠性的影响，得到了用于获取可靠DDM仿真波形的时延与多普勒间隔参数。研究表明，时延和多普勒间隔越小，DDM仿真波形与理论波形吻合度越高；当时延间隔取1/16个GPS L1 C/A码元、多普勒间隔取50 Hz时，DDM仿真波形与理论波形几乎完全吻合，二者的相关系数大于0.99；时延间隔对DDM仿真波形峰值位置的影响远大于对峰值信噪比的影响；而多普勒间隔对DDM仿真波形峰值位置与信噪比的影响相当。
Global Navigation Satellite System Reflectometry (GNSS-R) uses GNSS signals reflected by the Earth’s surface to detect the Earth surface parameters
including sea surface wind field
sea surface height
soil moisture
sea ice range
and snow depth. GNSS-R is a new Earth remote sensing technology
and it has been a research focus in recent years. Delay-Doppler Mapping (DDM) is a significant GNSS-R observation for retrieving geophysical parameters. The reliability of GNSS-R DDM simulation results directly affects GNSS-R theoretical research and satellite mission engineering parametric design. In this thesis
the effects of delay and Doppler intervals on a simulated DDM is investigated
and the suitable parameters for both intervals is determined to generate a reliable DDM simulation result. GREEPS
which was a GNSS-R simulator based on Z-V models and was developed by the National Space Science Center of the Chinese Academy of Sciences
is currently used to simulate DDM waveforms with different delay and Doppler intervals. To evaluate the accuracy of the simulated DDM waveforms
the 1D delay mapping with 0 Hz Doppler and the peak’s signal-to-noise ratio (SNR) and position of the simulated DDM are compared with theoretical ones. The results shows that the smaller the delay and Doppler intervals are
the higher the correlation coefficient of the simulated DDM waveform and the theoretical waveform will be. When the delay interval is less than 1/16 GPS L1 C/A code chips
the correlation coefficient of the simulated DDM waveform and the theoretical waveform is greater than 0.99. The relative deviation of the peak SNR of the simulated DDM waveform is approximately 0.1% when the delay interval is set to be 1/16 GPS L1 C/A code chips. However
the relative deviation of the peak position of the simulated DDM waveform is extremely high. Even if the delay interval is set to be 1/64 GPS L1 C/A code chips
the relative deviation of the peak position of the simulated DDM is just 2.3%. When the Doppler interval is less than 200 Hz
the correlation coefficient of the simulated DDM waveform and the theoretical waveform is close to 1. When the Doppler interval is less than 50 Hz
the relative deviation of the peak SNR and the peak position is less than 0.1%. It is can be concluded that the smaller the delay and Doppler intervals are
the higher the coincidence degree of the simulated DDM and the theoretical DDM will be. When the delay interval and the Doppler interval are set to be 1/16 GPS L1 C/A code chips and 50 Hz
respectively
the simulated waveforms and the theoretical waveforms highly coincide with one another. The correlation coefficient is more than 0.99
and the relative deviation of the peak SNR of DDM is less than 0.1%. The effect of the delay interval on the peak’s position of DDM is greater than that of the Doppler interval. The relative deviation of the position of the peak SNR is greater than 2% even if the delay interval is set to 1/64 GPS L1 C/A code chips.
Addabbo P, Giangregorio G, Galdi C and Di Bisceglie M. 2017. Simulation of TechDemoSat-1 delay-Doppler maps for GPS ocean reflectometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9): 4256–4268
Bai W H, Xia J M, Zhao D Y, Sun Y Q, Meng X G, Liu C L, Du Q F, Wang X Y, Wang D W, Wu D, Wu C J, Liu C and Cai Y R. 2016. GREEPS: an GNSS-R end-to-end performance simulator//2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing: IEEE: 4831–4834 [DOI: 10.1109/IGARSS.2016.7730260 http://dx.doi.org/10.1109/IGARSS.2016.7730260 ]
Cardellach E. 2002. Sea surface determination using GNSS reflected signals. Barcelona: Polytechnical University of Catalonia: 37–38
Clarizia M P, Gommenginger C P, Gleason S T, Srokosz M A, Galdi C and Di Bisceglie M. 2009. Analysis of GNSS-R delay-Doppler maps from the UK-DMC satellite over the ocean. Geophysical Research Letters, 36(2): L02068
Elfouhaily T, Chapron B, Katsaros K and Vandemark D. 1997. A unified directional spectrum for long and short wind-driven waves. Journal of Geophysical Research, 102(C7): 15781–15796
Foti G, Gommenginger C, Jales P, Unwin M, Shaw A, Robertson C and Roselló J. 2015. Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemoSat-1 mission. Geophysical Research Letter, 42(13): 5435–5441
Gleason S, Hodgart S, Sun Y P, Gommenginger C, Mackin S, Adjrad M and Unwin M. 2005. Detection and Processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1229–1241
Marchan-Hernandez J F, Camps A, Rodriguez-Alvarez N, Valencia E, Bosch-Lluis X and Ramos-Perez I. 2009. An efficient algorithm to the simulation of delay–Doppler maps of reflected global navigation satellite system signals. IEEE Transactions on Geoscience and Remote Sensing, 47(8): 2733–2740
Morris M and Ruf C S. 2017. Determining tropical cyclone surface wind speed structure and intensity with the CYGNSS satellite Constellation. Journal of Applied Meteorology and Climatology, 56(7): 1847–1865
O’Brien A and Johnson T J. 2017. Comparing the CYGNSS simulator forward scattering model with TDS-1 and CYGNSS on-orbit DDMS//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX, USA: IEEE: 2657–2658 [DOI: 10.1109/IGARSS.2017.8127542 http://dx.doi.org/10.1109/IGARSS.2017.8127542 ]
Park H, Camps A, Pascual D, Kang Y J, Onrubia R, Querol J and Alonso-Arroyo A. 2017. A generic level 1 simulator for spaceborne GNSS-R Missions and application to GEROS-ISS ocean reflectometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10): 4645–4659
Pierdicca N, Guerriero L, Giusto R, Brogioni M and Egido V. 2014. SAVERS: a simulator of GNSS Reflections from bare and vegetated soils. IEEE Transactions on Geoscience and Remote Sensing, 52(10): 6542–6554
Ruf C, Unwin M, Dickinson J, Rose R, Rose D, Vincent M and Lyons A. 2013. CYGNSS: enabling the future of hurricane prediction. IEEE Geoscience and Remote Sensing Magazine, 1(2): 52–67
Saïd F, Katzberg S J and Soisuvarn S. 2017. Retrieving hurricane maximum winds using simulated CYGNSS power-versus-delay waveforms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8): 3799–3809
Soisuvarn S, Jelenak Z, Said F, Chang P S and Egido A. 2016. The GNSS reflectometry response to the ocean surface winds and waves. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10): 4678–4699
Sun Y Q, Liu C L, Du Q F, Wang X Y, Bai W H, Kirchengast G, Xia J M, Meng X G, Wang D W, Cai Y R, Zhao D Y, Wu C J, Li W and Liu C. 2017. Global navigation satellite system occultation sounder II (GNOS II)//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX, USA: IEEE: 1189–1192 [DOI: 10.1109/IGARSS.2017.8127170 http://dx.doi.org/10.1109/IGARSS.2017.8127170 ]
Warnock A M, Ruf C S and Morris M. 2017. Storm surge prediction with CYGNSS winds//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX, USA: IEEE: 2975–2978 [DOI: 10.1109/IGARSS.2017.8127624 http://dx.doi.org/10.1109/IGARSS.2017.8127624 ]
Zavorotny V U, Gleason S, Cardellach E and Camps A. 2014. Tutorial on remote sensing using GNSS bistatic radar of opportunity. IEEE Geoscience and Remote Sensing Magazine, 2(4): 8–45
Zavorotny V U and Voronovich A G. 2000. Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 951–964
相关作者
相关机构
京公网安备11010802024621
