改善移动终端测量精度的卡尔曼滤波算法

Kalman filter algorithm for improving mobile terminal measurement accuracy

针对开采沉陷监测中常规RTK测量精度偏低的问题,结合开采沉陷监测的特点,以煤矿开采沉陷自动化监测系统的实时数据采集终端系统2min内各历元采集的坐标及精度为基础,构建卡尔曼滤波模型,以进一步改善移动终端测量精度。利用地表移动观测站的实测数据,通过与常规RTK测量、水准测量结果的比较分析,结果表明滤波后的测量精度得到了较好的改善。从内部符合精度来看,滤波RTK测量的平面位置平均测量精度约为±0.3cm,高程方向平均测量精度约为±0.5cm,完全满足开采沉陷监测的精度要求;与水准测量成果相比,高程方向平均外部测量精度约为±0.8cm,基本满足开采沉陷监测的精度要求。

For conventional RTK measurement accuracy is lower in mining subsidence monitoring,according to the characteristics of the coal mining subsidence monitoring,and basing on the coordinate and it＇s accuracy collected by the real-time data acquisition terminal system of the coal mining subsidence monitoring automation system,in order to further improve the mobile terminal measurement accuracy,the kalman filtering model was built.Using the measured data of surface movement observation station,and comparing with the results of the conventional RTK measurement and levelling measurement,the filtered measuring accuracy got better improvement.From the internal accuracy of filtering RTK measurement,the plane measurement average accuracy could be up to±0.3cm,and the height measurement average accuracy was about±0.5cm.This internal accuracy could fully meet the accuracy requirement of mining subsidence monitoring.Comparing with the results of the filtering RTK measurement and levelling measurement,the height measurement external accuracy was about±0.8cm,which basically meet the accuracy requirement of mining subsidence height monitoring.