自适应卡尔曼滤波高铁沉降观测数据处理模型研究  

Data Processing Model of Adaptive Kalman Filter for High-speed Rail Settlement Observation

作　　者：冯磊 FENG Lei(Liaoning Natural Resources Affairs Service Center,Shenyang 110000,China)

机构地区：[1]辽宁省自然资源事务服务中心,辽宁沈阳110000

出　　处：《测绘与空间地理信息》2020年第2期208-212,215,共6页Geomatics & Spatial Information Technology

摘　　要：在介绍国内外高铁沉降数据处理方面的研究现状的基础上,依次阐述了包括回归分析法、人工神经网络、灰色系统理论和时间序列分析法在内的经典沉降数据处理方法,着重讲解了标准卡尔曼滤波理论及其相关公式,介绍了两种自适应卡尔曼滤波理论:方差分量估计AKF、方差补偿AKF。本文针对某具体工程实例,分别基于MATLAB平台编写了一套标准卡尔曼滤波程序和一套自适应卡尔曼滤波程序,并运用程序对其作了相关分析。通过对比分析,证明了自适应KF的优越性,并得到一套在处理实际问题时具有一定可行性的模型。This paper introduces domestic and overseas high-speed railway settlement data processing based on the current research,are discussed including regression analysis,artificial neural network,grey system theory and time series analysis,classical settlement data processing method. In addition,this paper focuses on the standard Kalman filter theory and its related formulas. And the two adaptive Kalman filter theory are introduced: variance component estimation AKF,variance compensation AKF. In this paper,a set of standard Kalman filtering program and a set of adaptive Kalman filtering program are written based on the MATLAB platform for a specific project,and related analysis is made by using the program. Through the comparative analysis,it proves the superiority of the adaptive KF,and obtains a set of models which have a certain feasibility in dealing with the practical problems.

关 键 词：高铁沉降 卡尔曼滤波 自适应 MATLAB 

分 类 号：P25[天文地球—测绘科学与技术] TB22[天文地球—大地测量学与测量工程]