自适应卡尔曼滤波在地铁监测中的应用

目的通过滤波后的数据对比,验证自适应卡尔曼滤波在处理地铁变形监测数据工作中优于经典卡尔曼滤波.方法分别应用经典卡尔曼滤波和自适应卡尔曼滤波建立动态处理数据模型,对地铁变形监测数据进行处理,并与人工实测值进行对比.结果使用经典卡尔曼滤波处理后,数据精度提高38%,使用自适应卡尔曼滤波处理后,数据精度提高55%.结论自适应卡尔曼滤波与经典卡尔曼滤波相比,自适应卡尔曼滤波剔除噪声效果强与经典卡尔曼滤波,并且自适应卡尔曼滤波后数据整体变化平稳,与实测值吻合性较好,自适应卡尔曼滤波在处理沈阳地铁一号线监测数据中,优于经典卡尔曼滤波.

Application of the Adaptive Kalman Filter in Metro Monitoring Data Processing

Using the classical Kalman filter and the adaptive Kalman filter,the dynamic data processing models w ere built,respectively. The subw ay deformation monitoring data w ere processed and compared w ith the manual measured values. It is found that using the classical Kalman filter to process data,the accuracy is increased by 38%,how ever,using the adaptive Kalman filter,the accuracy is increased by 55%. Results show that using the adaptive filter to eliminate noise is more efficient than using classical Kalman filter and the data change smoothly,w hich are in good agreement w ith the measured values. It indicates that using the adaptive Kalman filter is more effective than using the classical Kalman filter for monitoring data in Shenyang subw ay.