Abstract: In view of the serious multi-path error and unreliable monitoring accuracy of GNSS in the deformation monitoring of super high-rise buildings, this paper constructs a data fusion algorithm based on Kalman filtering and RTS smoothing for the fusion of GNSS and accelerometer data by constructing a systematic trend separation and filtering and denoising model with tunable Q-factor wavelet transform. The dynamic deformation information in the fused displacement is extracted using the tunable factor Gabor wavelet transform, and the validity of the fusion model is verified by comparing with the dynamic displacement after the quadratic frequency domain integration of the accelerometer data. The simulation results show that the fusion displacement algorithm constructed in this paper can effectively restore the real data, the root mean square error of the fused displacement data is 0.088 5 mm, the correlation number is 0.993 4, and the signal-to-noise ratio is 17.53. Through the super high-rise building measured data, the method in this paper achieves the noise cancellation and the data fusion of GNSS and accelerometer, and is able to extract the dynamic deformation information in the fused data, which improves the accuracy of the deformation monitoring and provides an effective method for the analysis of dynamic deformation of super high-rise buildings.

Key words: tunable factor wavelet transform, kalman filter, frequency domain integration, data fusion, super high-rise building