Fractional Kalman filtering model on data of monitoring accelerometer deformation

The deformed or vibratory behaviors will exceed the threshold of building under the influence of external factors, so that it is necessary to monitor the variety of deformed body. Accelerometer is widely used in deformation monitoring due to small size and high sampling rate. In this paper, the fractional Kalman filter is introduced to update the accelerometer data. The influence of the order of different fractional derivatives on the filtering results of the accelerometer is studied and compared. The results show that when the system noise and measurement noise are fixed, using different derivative orders and comparing the filtering results under different derivative orders, the root mean square error of the fractional filtering model is smaller. Compare the filtering results under different noise variances. By comparing the errors of the two models, the image shows that the fractional Kalman filter model has better filtering performance than the standard Kalman filter model.