A Novel Time–Frequency Approach Based on the Noise Characterization for Structural Health Monitoring (SHM) Using GNSS Observations

Abstract

In this manuscript, a novel time-frequency approach based on noise characterization is proposed for Structural Health Monitoring (SHM) using Global Navigation Satellite System (GNSS) observations. The Allan variance (AVAR) is used to conduct a thorough analysis of GNSS observations, offering greater insight into the noise properties of the system. The results of this noise analysis are utilized to assess bridge movements and enhance the precision of the SHM system. The primary focus of the manuscript is the application of AVAR in GNSS-based SHM, and the results demonstrate the proposed approach’s efficacy in accurately assessing bridge movements. The AVAR analysis revealed that GNSS measurements are contaminated with quantization, white, flicker, and random walk noises, with white and flicker as the dominant noises and the others as secondary. The application of the Kalman Filter reduced the magnitude of white and flicker noise in measurements by an average of 69.3% and 62.6%, respectively. The dominant periods of dynamic movements, determined from the Least Squares Harmonic Estimation (LS-HE) analysis, were found to be within the range of 68.53–179.75 min. The findings of the proposed approach indicate that bridge movement changes amount to 11.48 cm, which is within the permissible design limits. This novel time–frequency approach, based on noise characterization using AVAR, holds significant potential for designing and implementing GNSS-based SHM systems.