GNSS-R sea level height estimation model based on the combination of VMD and WinLSP

Hu Yuan; Yuan Xintai; Liu Wei; Hu Qingsong; Jiang Zhihao; Zhong Licheng

doi:10.12284/hyxb2022139

Volume 44 Issue 11

Nov. 2022

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Hu Yuan，Yuan Xintai，Liu Wei, et al. 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

Citation:

Hu Yuan，Yuan Xintai，Liu Wei, et al. 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

Citation:

Hu Yuan，Yuan Xintai，Liu Wei, et al. 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

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GNSS-R sea level height estimation model based on the combination of VMD and WinLSP

doi: 10.12284/hyxb2022139

1.
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2.
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

Received Date: 2021-09-11
Rev Recd Date: 2022-06-21

Available Online: 2022-08-02

Publish Date: 2022-11-03

Abstract

Abstract

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

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References(25)

References

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