基于卡尔曼滤波模型的地铁沉降监测分析

Monitoring and Analysis of Subway Settlement Based on Kalman Filter Model

卡尔曼滤波方法是变形监测中常用的一种方法,本文通过对获取的地铁沉降观测数据进行监测,采用经典的卡尔曼滤波方法和基于方法补偿的自适应卡尔曼滤波方法进行监测预估,通过对比发现,基于方差补偿的自适应卡尔曼滤波方法能够有效地控制观测数据异常对动态系统参数估计的影响,保证了变形监测数据估计的精度。

Kalman filtering method is a commonly used method in deformation monitoring.In this paper, by monitoring the data of metro subsidence obtained, the classical Kalman filter method and adaptive Kalman filtering method based on method compensation are used to monitor and estimate.By the comparison, it is found that the adaptive Kalman filter based on variance compensation can effectively control the influence of observation data anomaly on the estimation of dynamic system parameters and ensure the accuracy of deformation monitoring data estimation.