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

A BP neural network and sea surface wind speed technology, applied in the field of atmospheric science research, can solve problems such as the impact of accuracy, achieve high accuracy results, shorten inversion time, and efficiently invert sea surface wind speed

Pending Publication Date: 2020-09-08
NAT SPACE SCI CENT CAS
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, the sea surface wind speed is often not determined by only one or two parameters, so the accuracy of this method will be affected by ignoring other physical parameters

Method used

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  • GNSS-R sea surface wind speed inversion method and system based on BP neural network
  • GNSS-R sea surface wind speed inversion method and system based on BP neural network

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Embodiment 1

[0045] Such as figure 1 As shown, embodiment 1 of the present invention provides a kind of GNSS-R sea surface wind speed retrieval method based on BP neural network, mainly comprises the following steps:

[0046] The first step: the construction of the original data sample set. Match the GNSS-R data and ECMWF data spatiotemporally to form the original sample set;

[0047] A large number of GNSS-R data and ECMWF analysis field data are selected for spatiotemporal matching to obtain the original sample set. Each set of samples is composed of a DDM map and corresponding wind speed information.

[0048] Step 2: Generate training set and test set. Preprocess the original sample set and divide it into training set and test set;

[0049] In order to avoid data anomalies and noise interference, it is necessary to screen the longitude and latitude, wind speed, and signal-to-noise ratio (SNR) of the data; after screening, the problem of uneven distribution and inconsistent dimensions o...

Embodiment 2

[0066] Embodiment 2 of the present invention provides a kind of GNSS-R sea surface wind speed inversion system based on BP neural network, and described system comprises: trained sea surface wind speed inversion model and wind speed inversion module; Said sea surface wind speed inversion model is a BP neural network;

[0067] The wind speed inversion module is used to input the DDM map to be tested into the pre-trained sea surface wind speed inversion model, and output the corresponding inversion wind speed.

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Abstract

The invention discloses a GNSS-R sea surface wind speed inversion method and system based on a BP neural network, and the method comprises the steps: inputting a to-be-measured DDM image into a pre-trained sea surface wind speed inversion model, and outputting a corresponding inversion wind speed; wherein the sea surface wind speed inversion model is a BP neural network. According to the method, the GNSS-R sea surface wind speed is inverted by utilizing the BP neural network, the model is simple, the modeling time and the inversion time are shortened, and the inversion precision is further improved; the BP neural network makes full use of the physical quantity related to the wind speed in the DDM graph to perform feature learning, reduces the calculated amount and shortens the time consumption under the condition of ensuring the inversion precision, and has the characteristics of simple and rapid model, high result precision and the like.

Description

technical field [0001] The invention relates to the field of atmospheric science research, in particular to a GNSS-R sea surface wind speed inversion method and system based on a BP neural network. Background technique [0002] Sea surface wind speed is a crucial physical parameter in ocean state information, which can be detected by GNSS-R satellite remote sensing technology at present. Because GNSS-R technology has the characteristics of high global coverage and high temporal and spatial resolution, high-quality sea surface wind speed detection data can be obtained. At present, there are two main methods for GNSS-R wind speed retrieval: [0003] Waveform matching method: firstly, the system state information needs to be extracted according to the measured data, then the simulated waveform of the theoretical model is generated, and finally the theoretical waveform is obtained by normalization processing, and the simulation waveform database is established based on a large ...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/62
CPCG06N3/084G06N3/045G06F18/22Y02A90/10
Inventor 白伟华王斯嘉孙越强杜起飞刘黎军李伟王先毅蔡跃荣曹光伟夏俊明孟祥广柳聪亮赵丹阳尹聪胡鹏王冬伟刘成吴春俊李福乔颢程双双朱光武
Owner NAT SPACE SCI CENT CAS
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