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

作　　者：Yiwen Zhang Wei Zheng Zongqiang Liu 

机构地区：[1]Qian Xuesen Laboratory of Space Technology,China Academy of Space Technology,Beijing 100094,China [2]China Academy of Aerospace Science and Innovation,Beijing 100176,China [3]State Key Laboratory of Space-Ground Integrated Information Technology,Beijing Institute of Satellite Information Engineering,Beijing 100194,China

出　　处：《Defence Technology（防务技术）》2024年第2期271-284,共14页Defence Technology

基　　金：the National Natural Science Foundation of China under Grant(42274119);the Liaoning Revitalization Talents Program under Grant(XLYC2002082);National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(2022YFF1400500);the Key Project of Science and Technology Commission of the Central Military Commission.

摘　　要：Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric 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 75and 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.

关 键 词：GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network 

分 类 号：TN96[电子电信—信号与信息处理]