ADAPTIVE KALMAN FILTERING FOR THE DATA FUSION OF DYNAMIC RESPONSE DATA
Structural health monitoring (SHM) using vibration measurements has attracted considerable attention because of the availability of both acceleration and displacement measurements, from which the structural character could be found or identified. However the sensors are always affected by the noises. The existing of noise will severely affect the identifying accuracy. In order to get more accurate data, we may fuse the datum from different sensors, among which there is a certain relations to be utilized to increase the accuracy of the monitoring data. An adaptive Kalman filtering algorithm to fuse vibration response data collected at different sensing node is suggested. A shear frame model is developed to get the seismic response of different position to simulate the true sensing signal, to add white Gaussian noise to simulate polluted signal, and then to proceed the polluted signals according to the suggested algorithm. The comparisons among the fused, the polluted and the true signals show the feasibility and adaptability of the algorithm.
Kalman filter data fusion dynamic system adaptability different sensing node algorithm
C. Han M. Zhao
College of Civil Engineering, Tongji University, Shanghai, China
国际会议
上海
英文
1509-1517
2007-11-28（万方平台首次上网日期，不代表论文的发表时间）