首页|Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

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Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constel-lation platforms has become a new research direction and inevitable trend,which can meet the alti-metric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75° and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

GNSS-R satellite constellationsSea surface altimetric precisionUnderwater navigationMultilayer feedforward neural network

Yiwen Zhang、Wei Zheng、Zongqiang Liu

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Qian Xuesen Laboratory of Space Technology,China Academy of Space Technology,Beijing 100094,China

China Academy of Aerospace Science and Innovation,Beijing 100176,China

State Key Laboratory of Space-Ground Integrated Information Technology,Beijing Institute of Satellite Information Engineering,Beijing 100194,China

the National Natural Science Foundation of Chinathe Liaoning Revitalization Talents ProgramNational Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integrationthe Key Project of Science and Technology Commission of the Central Military Commission

42274119XLYC20020822022YFF1400500

2024

10.1016/j.dt.2023.03.019

防务技术
中国兵工学会

防务技术

CSTPCD
影响因子：0.358
ISSN：2214-9147
年,卷(期)：2024.32(2)
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