摘要
由于传统卡尔曼滤波处理实时动态数据中所建立的数学模型不精确或动态噪声特性不准确,导致状态估计失真,甚至导致滤波发散的现象。为了提高高层建筑物沉降监测实时预测的准确性与可靠性,克服传统卡尔曼滤波进行沉降预测中噪声的不足,本文采用方差补偿自适应卡尔曼滤波理论进行分析预测,并通过实测数据对比分析,验证了方差补偿自适应卡尔曼滤滤波理论进行沉降预测的可行性,且预测精度较高。
Because of the established mathematical model of traditional Kalman filter processing implementation of the dynamic data of imprecise or dynamic noise characteristics of inaccurate, resulting in state estimation distortion, even leads to filter divergence phenomenon. In order to improve the high-rise building subsidence monitoring real-time forecasting accuracy and reliability, this paper adopts variance compensation adaptive Kalman filter theory analysis and forecasting, to overcome the traditional Kalman filter was insufficient in settlement prediction of noise, and through the comparison of ex perimental data analysis, to verify the variance compensation adaptive Kalman filter filter theory feasibility settlement prediction, and the prediction precision higher.
出处
《北京测绘》
2015年第3期51-54,共4页
Beijing Surveying and Mapping
关键词
自适应卡尔曼滤波
方差补偿
沉降监测
adaptive kalman filter
variance compensation
subsidence monitoring
