基于Kalman滤波的高耸建筑物沉降预测模型研究

为了提高高耸建筑物沉降监测数据的可信性与实时预测的准确性,利用小波分析与Kalman滤波在数据处理方面的优势,采用小波分析先对沉降数据进行去噪,并采用Kalman滤波理论进行预测。通过实验数据的对比分析,结合小波去噪分析与Kalman滤波理论对原始沉降变形量进行沉降预测,能克服只使用单一Kalman滤波方法进行沉降预测中噪声处理的不足,验证了小波分析及结合Kalman滤波理论进行沉降预测的可行性,且预测精度比较高。

Research on settlement prediction model of high-rise buildings based on Kalman filter

In order to improve the reliability of the monitoring data of high‐rise building subsidence and the accuracy of its real time prediction ,it presents the advantage of wavelet analysis and Kalman filtering in the aspect of data processing .Firstly ,it denoises the settlement data using wavelet analysis and carries on the forecast using Kalman filtering theory .Then this paper predicts the original settlement deformation throught the comparative analysis of experimental data ,combined with wavelet denoising analysis and Kalman filter theory . T his method can overcome the disadvantage of single Kalman filtering method on noise processing in settlement prediction ,and verify the feasibility of Kalman filtering theory combined with wavelet analysis in the settlement prediction ,as results of which the prediction is more accuracy .