Article citationsMore>>

Liu, X., Bai, W., Xia, J., Huang, F., Yin, C., Sun, Y., et al. (2021) FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval. Remote Sensing, 13, Article No. 4820. https://doi.org/10.3390/rs13234820

has been cited by the following article:

• TITLE: GNSS Reflectometry Global Ocean Wind Speed Estimation Based on CyGNSSMsa

  AUTHORS: Weimin Chen, Bin Wang, Dongmei Song

  KEYWORDS: GNSS-R, CYGNSS, Ocean Wind Speed

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.6, June 30, 2025

  ABSTRACT: GNSS Reflectometry (GNSS-R) technology utilizes existing navigation satellite signals as opportunistic microwave illumination sources, eliminating the need for dedicated transmitters and thereby significantly reducing hardware costs. Benefiting from the global coverage of navigation constellations, GNSS-R enables worldwide sea surface wind monitoring with superior spatiotemporal resolution. The acquired Delay-Doppler Maps (DDMs) encode sea surface roughness characteristics near the specular reflection point, which are intrinsically modulated by wind-driven capillary waves. We proposed CyGNSSMsa employs CyGNSSnet’s dual-branch framework for preliminary feature extraction from DDM and auxiliary parameters, followed by multihead self-attention (MSA) mechanisms to decipher long-range dependencies within fused feature representations. Multi-scale feature fusion is achieved through residual connections across hierarchical network layers, culminating in precise wind speed regression via multilayer perceptrons. Experimental validation demonstrates CyGNSSMsa’s performance with an RMSE of 1.345 m/s, achieving simultaneous improvements in both accuracy and systematic bias mitigation.