Maximum Likelihood Estimation of Ocean Wind Vector Using Subsatellite-Observation Spaceborne Global Navigation Satellite System-Reflectometry

WANG Feng; LI Jianqiang; ZHANG Guodong; ZHANG Qi; YANG Dongkai

doi:10.11999/JEIT230464

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1418-1427

WANG Feng, LI Jianqiang, ZHANG Guodong, ZHANG Qi, YANG Dongkai. Maximum Likelihood Estimation of Ocean Wind Vector Using Subsatellite-Observation Spaceborne Global Navigation Satellite System-Reflectometry[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1418-1427. doi: 10.11999/JEIT230464

Citation:

WANG Feng, LI Jianqiang, ZHANG Guodong, ZHANG Qi, YANG Dongkai. Maximum Likelihood Estimation of Ocean Wind Vector Using Subsatellite-Observation Spaceborne Global Navigation Satellite System-Reflectometry[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1418-1427. doi: 10.11999/JEIT230464

Citation:

WANG Feng, LI Jianqiang, ZHANG Guodong, ZHANG Qi, YANG Dongkai. Maximum Likelihood Estimation of Ocean Wind Vector Using Subsatellite-Observation Spaceborne Global Navigation Satellite System-Reflectometry[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1418-1427. doi: 10.11999/JEIT230464

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Maximum Likelihood Estimation of Ocean Wind Vector Using Subsatellite-Observation Spaceborne Global Navigation Satellite System-Reflectometry

doi: 10.11999/JEIT230464

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2.
School of Astronautics, Beihang University, Beijing 100191, China
3.
Shanghai Academy of Spaceflight Technology, Shanghai 201109, China
4.
Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing 100094, China
5.
Shanghai ASES Spaceflight Technology Co. Ltd., Shanghai 201108, China

Funds: China National Postdoctoral Program for Innovative Talents (BX20200039), Shanghai Industrial Collaborative Innovation Project (2021-cyxt2-kj05)

Received Date: 2023-05-22
Rev Recd Date: 2023-12-15

Available Online: 2023-12-23

Publish Date: 2024-04-24

Abstract

Abstract

It is difficult to retrieve the ocean wind direction using the specular reflection signal owing to its insensitivity to the sea surface wind direction. The sensitivity of the scattered Global Navigation Satellite System-Reflectometry (GNSS-R) signal from the non-specular geometry to wind direction is first investigated in this paper. The sub-satellite non-specular observation mode is proposed, and the observable quanitity sensitive to wind direction in this mode is defined. Based on this mode, the wind vector retrieval algorithm using spaceborne GNSS-R based on the Maximum Likelihood Estimation (MLE) is presented, in which the sub-satellite non-specular scattering signals from two and more navigation satellites are used to retrieve ocean wind vector. A simulator is developed to demonstrate and test the proposed algorithm. The results show that due to the ambiguity of the observation geometry and the symmetry of the ocean spectrum, there are four uncertain solutions in the retrieved wind directions. The ambiguity of the measurement geometry can be eliminated by using the multi-satellite observation, and only two possible solutions of wind directions still are remained. When the scattered signals from three satellites are used, and the Signal-to-Noise Ratio (SNR) is over 11 dB, the Root Mean Square Error (RMSE) of the retrieved wind speed and direction are less 2 m/s and 15° with a wind speed rang of 2～25 m/s.
- Global Navigation Satellite System-Reflectometry (GNSS-R),
- Maximum Likelihood Estimation (MLE),
- Ocean wind vector,
- Remote sensing

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References(27)

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