A fractional-order Kalman filter extraction model for dynamic deformation of super-high buildings
Global navigation satellite system(GNSS)has been widely used in dynamic deformation monitoring of super-high buildings because of its advantages of direct measurement of three-dimensional(3D)coordinates without access to view.When GNSS is used to monitor buildings,the monitoring data usually contains a lot of noise.In order to extract the characteristics of deformation information more accurately,this paper used a fractional-order Kalman filter(FKF)to process the data.Through the analysis of simulation experiment data and actual case data,the correlation coefficient,root mean square error,and other evaluation parameters were compared with the Kalman filter(KF)results,so as to verify the feasibility of the method.The results show that FKF is more effective than the KF model in extracting deformation information of super-high buildings.