Electronics and Electrical Engineering and Control

GNSS-R sea surface wind speed inversion based on BP neural network

  • GAO Han ,
  • BAI Zhaoguang ,
  • FAN Dongdong
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  • DFH Satellite Co., Ltd., Beijing 100094, China

Received date: 2019-07-02

  Revised date: 2019-07-22

  Online published: 2019-08-29

Abstract

In the sea surface wind speed inversion of GNSS-R, the time-frequency domain related physical quantity is large, and the data coupling is strong. A method of inversion of sea surface wind speed based on Back-Propagation (BP) neural network is proposed. This paper establishes the corresponding relationship between the correlation observation and the wind speed in the inversion process. Selecting the multi-view measurement as the input, the input data are processed, the neuron and the excitation function are set. using the BP neural network, the fitting parameters are adaptively adjusted. And the wind speed is used as the extraction feature in the neural network. The inversion results show that when the wind speed is ≤ 20 m/s, the inversion Root Mean Square Error (RMSE) is 1.21 m/s, and the inversion RMSE is 2.54 m/s when the wind speed is >20 m/s. The result is better than the inversion results obtained by the Delay Leading Edge Slope and Delay-Doppler Average methods, and the number of iterations is small and the complexity is low, proving that the method can be applied to GNSS-R sea surface wind speed inversion.

Cite this article

GAO Han , BAI Zhaoguang , FAN Dongdong . GNSS-R sea surface wind speed inversion based on BP neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019 , 40(12) : 323261 -323261 . DOI: 10.7527/S1000-6893.2019.23261

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