Ten-Minute Sea-Level Variations From Combined Multi-GNSS Multipath Reflectometry Based on a Weighted Iterative Least-Square Method
Mingda Ye, Shuanggen Jin, Yan Jia
Abstract
Accurate and high-frequency sea level monitoring is of great importance in ocean environments and global climatic studies, but traditional techniques have their respective limitations. In the last decades, the application of Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) in sea level monitoring has developed rapidly. Recently, more available GNSS signals are expected to bring new opportunities to improve its performance and achieve high spatial-temporal resolution. In this paper, a new algorithm is developed to optimize the method of multi-GNSS multipath reflectometry and improve the precision and sampling rate for GNSS-MR sea level monitoring. In order to make full use of the short-term multipath oscillation information, a sliding window is used to collect the SNR sequences. A weighted iterative least-square method is introduced to combine the selected SNR observations of GPS, GLONASS, Galileo, and BDS systems, and retrieve sea level with 10-minute intervals at BRST station for one year. A novel index called Local Kurtosis (LK) is proposed, which can be used to evaluate the quality of the Lomb-Scargle periodogram (LSP) and design the weight matrix in the least-square combinatorial process. Compared to using individual signals, the optimized combination algorithm decreased the root mean square error (RMSE) by 78%, from 0.610 m to 0.134 m, and increased the correlation coefficient R<sup>2</sup> from 0.851 to 0.992. In addition, the tidal constituents monitored by multi-GNSS-MR and tide gauge are highly consistent, demonstrating that the multi-GNSS-MR can accurately retrieve daily and subdaily tidal constituents of periods longer than 10 min.