| 利用新息构造AKF估计和预报大坝变形 |
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| 引用本文： | 李振,朱锋,陈家君.利用新息构造AKF估计和预报大坝变形[J].武汉大学学报(信息科学版),2012,37(4):454-457. |
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| 作者姓名： | 李振 朱锋 陈家君 |
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| 作者单位： | 武汉大学测绘学院,武汉市珞喻路129号,430079 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 利用线形流形的射影方法推导出新息序列统计特性，构造新息AKF，基于新息序列不断地修正状态噪声和量测噪声，实时地反映模型当前真实的统计特性。通过隔河岩大坝实测数据处理，表明该方法能很好地提高随机模型不准确和变形突变影响下的变形估计与预报精度。
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| 关 键 词： | 新息AKF 状态噪声 量测噪声 变形监测 |
Construct AKF to Improve Precision of Estimating and Predicting Dam Deformation Using Innovation |
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| LI Zhen,ZHU Feng,CHEN Jiajun.Construct AKF to Improve Precision of Estimating and Predicting Dam Deformation Using Innovation[J].Geomatics and Information Science of Wuhan University,2012,37(4):454-457. |
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| Authors: | LI Zhen ZHU Feng CHEN Jiajun |
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| Institution: | 1(1 School of Geodesy and Geomatics,Wuhan University,129 Luoyu Road,Wuhan 430079,China) |
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| Abstract: | The Kalman filter(KF) method is propitious to process the dynamic dam deformation monitoring data.However,the state noise(R) and survey noise(Q) in the deformation monitoring is difficult to be provided precisely,the standard Kalman Filter method is confined.This article presents AKF based on innovation statistic properties to modify the R and Q,and to reflect correct statistic properties of current model by real time.With a dam survey data,this method succeed to overcome imprecision of stochastic model and deformation saltation to improve the precision of estimation and prediction in dam deformation. |
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| Keywords: | innovation AKF state noise survey noise deformation monitoring |
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