Abstract:Real-time high-precision displacement measurement is important for the safety and life-cycle assessment of engineering structures. To improve the accuracy and stability of displacement measurement based on Global Navigation Satellite System (GNSS) technology, an adaptive multi-rate Kalman filter is proposed to fuse the acceleration and displacement data. Due to unreasonable settings of noise parameters, the accuracy of displacement estimation can be seriously degraded. By utilizing the characteristics of acceleration and displacement measurement noises, the adaptive estimation is realized through estimating the variance of their corresponding noises separately. Considering the noise characteristics of accelerometer and GNSS device, the estimation of noise parameters in the adaptive filter is simplified to estimate only the variance of displacement noise. The Sage-Husa estimator is used to realize the adaptive estimation of displacement noise variance so that the filter can reach a stable real-time displacement estimation under inaccurate noise parameters. First, the settings of initial noise parameters in the proposed adaptive filter are discussed to determine its rule. Then the displacement estimation performance of the filter at different signal frequencies is discussed through the harmonic displacement under time-invariant noise and time-varying noise. Finally, the effectiveness of the proposed technique is demonstrated by using a numerical simulation response from a 1.5 MW wind turbine tower under wind-earthquake coupling. The results show that even if the initial noise parameters are inaccurate and the displacement measurement noise is time-varying, the proposed technique still has satisfactory performance and robustness in real-time estimation. This research can provide a reference for real-time and high-precision displacement monitoring of structures.