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

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 75 degrees 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. (c) 2024 China Ordnance Society. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).