自适应卡尔曼滤波在基坑变形监测中的应用

摘要

Kalman filtering is the most used method of data processing of deformation monitoring in present. If adopted inaccurate mathematical models and noise design, kalman filtering may lead to state estimation distortion, even lead to filter divergence. The article presents to create monitor data processing theories, methods and models with a variance compensation self-adapting kalman filtering theory, Then processing the displacement monitoring data in a building Foundation ditch of Taiyuan, Wanda Plaza and obtaining the displacement change tendency.%在现代建筑中基坑越来越深，基坑越深带来的危险就越大，因此准确地监测和预报基坑的变形趋势十分重要。卡尔曼滤波是经典的数据处理方法，但传统卡尔曼滤波所建立的数学模型不是很精确且动态噪音设计不易确定可能导致状态估计失真，甚至导致滤波发散的现象，本文利用方差补偿自适应卡尔曼滤波理论对太原万达广场某商住楼基坑位移监测数据进行处理，得出基坑位移变化趋势。