Research on Wavelet Denoising and Kalman Filter in Bridge
Deformation Monitoring Data
1 College of Geomatics, Shandong University of Science and Technology, 576 Qianwangang Road, Qingdao 266590, China
2 Xin’an Coal Mine of Zaozhuang Mining Group, Liuzhuang Town, Weishan Couty, Jining 277642, China
3 Quanxing Mining Group of Shandong Province, High-Tec Zone,Zaozhuang 277000, China
Abstract With respect to 3D deformation monitoring of a sea-crossing bridge, the schemes of measuring robot and 3D laser scanner are implemented. For both, the maximum range of 3D deformation monitoring data is less than 0.5mm. The monitoring data of the measuring robot is high precision, and the 3D laser scanner technology applied to bridge deformation monitoring is feasible. Based on the wavelets and Kalman filtering theory, the Kalman filtering model of bridge deformation monitoring after wavelet denoising is established. The experiment shows that applying Kalman filtering through the monitoring data after wavelet de-noising processing improves the reliability of the deformation analysis. Comparison of the results obtained by the methods of standard Kalman filter data and adaptive Kalman filtering, and the analysis of sum of squares error, mean square error and average relative error precision, shows that the adaptive Kalman filtering method is superior to the standard Kalman filtering method.
LUAN Yuanzhong,LUAN Hengxuan,LI Wei et al. Research on Wavelet Denoising and Kalman Filter in Bridge
Deformation Monitoring Data[J]. jgg, 2015, 35(6): 1041-1045.
LUAN Yuanzhong,LUAN Hengxuan,LI Wei et al. Research on Wavelet Denoising and Kalman Filter in Bridge
Deformation Monitoring Data[J]. jgg, 2015, 35(6): 1041-1045.
