基于BP神经网络的GNSS-R海面风速反演

doi:10.7527/S1000-6893.2019.23261

Abstract

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.

Key words: GNSS-R, sea surface wind speed, Back-Propagation (BP), neural networks, inversion mode

CLC Number:

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.

References 30

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

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