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GNSS-R sea level height estimation model based on the combination of VMD and WinLSP
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Yuan Hu1, Xintai Yuan1, Wei Liu2, *, Qingsong Hu1, Zhihao Jiang1, Licheng Zhong1
Haiyang Xuebao | 2022, 44(11) : 170 - 178
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Haiyang Xuebao | 2022, 44(11): 170-178
Article
GNSS-R sea level height estimation model based on the combination of VMD and WinLSP
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Yuan Hu1, Xintai Yuan1, Wei Liu2, *, Qingsong Hu1, Zhihao Jiang1, Licheng Zhong1
Affiliations
  • 1. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
  • 2. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Published: 2022-11-01 doi: 10.12284/hyxb2022139
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Global navigation satellite system-reflectometry (GNSS-R) technology is an emerging technology for monitoring sea level changes. Based on the principle of the signal to noise ratio (SNR) analysis method in GNSS-R technology, this paper established a new sea level height estimation model to improve the accuracy by analyzing the process of separating the trend term and extracting the oscillation frequency. Aiming at the problem of poor signal separation in the traditional model, this paper proposed to use the variational mode decomposition (VMD) algorithm to replace the traditional least squares fitting (LSF) to separate the trend term components. On this basis, this paper combined Lomb-Scargle Periodogram (LSP) spectral analysis method and Kaiser window function (referred to as WinLSP) to reduce the inversion error caused by spectral leakage. The results of sea level inversion experiments carried out at GTGU Station in Onsala, Sweden and SC02 Station in Alaska, USA show that the estimation model established in this paper has higher inversion accuracy than traditional model. The root mean square error (RMSE), correlation coefficient and number of inversion points of the inversion results of GTGU Station based on the VMD+WinLSP estimation model are 4.70 cm, 0.98 and 5 647, respectively. The inversion accuracy and GNSS data utilization are increased by about 29.7% and 15.0%, respectively; The RMSE, correlation coefficient, and inversion points of SC02 Station are14.34 cm, 0.99 and 1 785, respectively, and the inversion accuracy and GNSS data utilization are increased by about 12.3 % and 9.4%.

global navigation satellite system-reflectometry  /  signal to noise ratio  /  variational mode decomposition  /  Kaiser window function
Yuan Hu, Xintai Yuan, Wei Liu, Qingsong Hu, Zhihao Jiang, Licheng Zhong. GNSS-R sea level height estimation model based on the combination of VMD and WinLSP[J]. Haiyang Xuebao, 2022 , 44 (11) : 170 -178 . DOI: 10.12284/hyxb2022139
Year 2022 volume 44 Issue 11
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doi: 10.12284/hyxb2022139
  • Receive Date:2021-09-11
  • Online Date:2026-02-01
  • Published:2022-11-01
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  • Received:2021-09-11
  • Revised:2022-06-21
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    1. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
    2. Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
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表12种不同金属材料的力学参数

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Number of
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种数
Number of
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占总种数比例
Percentage of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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