An improved GNSS/accelerometer deformation monitoring algorithm using the Savitzky–Golay (SG) denoising tilt angle information

Abstract GNSS and accelerometer technologies are widely used in deformation monitoring, such as earthquakes, landslides and bridges. However, the positioning accuracy of traditional GNSS and accelerometer Kalman filters may be decreased due to the continuous changes of ground tilt during deformation growth. To address this issue, we propose an improved GNSS/accelerometer deformation monitoring algorithm based on denoised tilt angle information. Firstly, since the calculation of the tilt angle by the accelerometer is highly susceptible to external motions such as ground vibrations caused by landslides, this paper employs the Savitzky–Golay filter to enhance the accuracy of the tilt angle calculated by the accelerometer. Then, an angle cosine matrix is constructed based on the obtained tilt angle to correct the original accelerometer signals. In this way, the positioning results can be improved through the fusion of GNSS and cleaner acceleration data. The simulated deformation experiments were conducted to validate the proposed algorithm.The results showed that the root mean squared error (RMSE) of pitch and roll angles were 0.185° and 0.169° in low dynamic conditions, respectively, which were improved by 76.36% and 76.13%, respectively, compared with the traditional method. Furthermore, the positioning RMSE of East–North–Up ( E, N and U ) directions presented 0.011 m, 0.009 m, and 0.006 m, respectively, which were 88.78%, 95.93% and 91.78% over that of the traditional Kalman filter. Furthermore, the algorithm has been effectively verified in a real landslide environment. Due to the acquisition of more ‘pure’ acceleration data after inclination correction, the positioning accuracies of the proposed algorithm in the E, N and U directions have been increased by 16.22%, 37.20%, and 83.73%, respectively, compared with the traditional Kalman filtering algorithm.