A Temporal and Spatial Denoising Method for Intelligent Settlement Sensing System

Global Navigation Satellite System (GNSS) has the ability to provide timely structural settlement information and been widely installed on the critical infrastructures. However, the inevitable noise negatively affects the accurate assessment, measurement and evaluation of the status. Although some methods have been developed in previous studies, fully mining the temporal and spatial correlations is still necessary to further analyze. Therefore, we propose a new denoising method based on truncated high-order singular value decomposition to reduce the noise in multivariate GNSS signals. Using synthetic signals and real-world GNSS signals, the proposed method is evaluated by comparison with some benchmark models. The results show that the proposed method can effectively reduce both white noise (WN) and flicker noise (FN). Moreover, experiments on the signal-noise ratio (SNR) from 1 to 10 demonstrate that the proposed method can achieve stable performances for GNSS time series denoising. The organization of GNSS signals as the high-dimensional tensor has been proved to be an effective analytical tool to mine the complicated spatial and temporal correlations.

Automated summary

Global Navigation Satellite System (GNSS) is widely used in critical infrastructures to provide timely structural settlement information. However, the accurate assessment, measurement, and evaluation of the status are affected by the inevitable noise. This study proposes a new denoising method based on truncated high-order singular value decomposition to effectively reduce the noise in multivariate GNSS signals.