桂林理工大学学报2017，Vol.37Issue(4)：653-657,5.DOI:10.3969/j.issn.1674-9057.2017.04.016
基于卡尔曼滤波的灰色马尔科夫组合模型在基坑变形监测中的应用
Application of gray Markov combined model based on Kalman filter in foundation pit deformation monitoring
摘要
Abstract
The study focuses on monitoring data influenced by random disturbance in the foundation pit deformation monitoring.At first,Kalman filter is adopted to process the monitoring data,to eliminate the disturbance error and extract the deformation data that could reflect the deformation of the retaining structure accurately from the monitoring data.Then the extracted data was used to establish the gray Markov model for the deformation prediction.According to the foundation pit deformation monitoring,compared to the traditional gray model and gray Markov model,the gray Markov Model based on the Kalman filter significantly can increase accuracy of the prediction.It can solve the problem of random disturbance in foundation pit monitoring,and has some practical value.关键词
灰色模型/卡尔曼滤波/马尔科夫链/基坑监测/预测Key words
gray model/Kalman filter/Markov chain/foundation pit deformation/prediction分类
天文与地球科学引用本文复制引用
朱军桃,熊东旭,李亚威..基于卡尔曼滤波的灰色马尔科夫组合模型在基坑变形监测中的应用[J].桂林理工大学学报,2017,37(4):653-657,5.基金项目
国家自然科学基金项目(41071294) （41071294）
广西高校科学技术研究重点项目(ZD2014062) （ZD2014062）
