Fusion of PSO-SVM and ICEEMDAN for high stability GNSS-MR sea level height estimation

Abstract

ABSTRACTThe Global Navigation Satellite System (GNSS) Multipath Reflection (MR) technique utilises the multipath effects of GNSS signals on the sea surface to retrieve tidal variations, playing a crucial role in tidal monitoring. However, traditional GNSS-MR techniques have certain limitations in terms of accuracy and stability due to restrictions in satellite elevation angles and antenna heights. This study proposes a new GNSS-MR sea surface height retrieval method that combines Particle Swarm Optimization (PSO) optimised Support Vector Machine (SVM) with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The method utilises the GNSS multipath interfering signal frequencies (signal-to-noise oscillation term) extracted by ICEEMDAN as input features to the PSO-SVM model to retrieve the sea level height. Using one year of signal-to-noise ratio data from GNSS stations SC02 and TPW2, the stability and accuracy of the proposed method are evaluated under conditions of high satellite elevation angles and without precise GNSS antenna height information. Experimental results demonstrate that the PSO-SVM-ICEEMDAN method is superior to other existing methods in aspects of retrieval stabilities, root mean square errors, consistent tidal patterns, and so on.

Keywords

• Sea level height estimation
• GNSS-MR
• ICEEMDAN
• PSO
• SVM