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It is hoped that this research will contribute to a deeper understanding of displacement extraction in SHM.

On the one hand, long-term displacement is ensured, on the other hand, the sensitivity of short-term displacement detection will not be reduced. The findings show that the low-frequency displacement or absolute position is maintained by the Kalman filter of GNSS kinematic positioning, and the dynamic feature or high-frequency displacement is extracted by the IMM Kalman filter of the accelerometer. Three groups of experiments were carried out to verify the proposed method. The methodological approach taken in this study is a mixed methodology based on the interactive multiple model (IMM) Kalman filter. The present study aimed to explore an on-demand fusion method with GNSS and accelerometer measurement. Few empirical studies have focused on the fusion algorithm of slight displacement extraction. Most studies in the field of fusion algorithms between GNSS and accelerometer for structural health monitoring have only focused on large displacement extraction. There has been substantial research undertaken on the data fusion of GNSS and accelerate for structural health monitoring. The data fusion of GNSS and accelerometer is conducive to taking advantage of both for SHM.

Global Navigation Satellite System (GNSS) can provide absolute position, but the sampling rate is limited while the accelerometer has a higher sampling rate, its displacement is obtained through double integration, which is not suitable for low-frequency deformation monitoring. Displacement extraction of structures is of great significance to structural health monitoring (SHM).