基于EEMD-FastICA的位移摄像测量误差源探究

来源:用户上传      作者:周华飞 蒋贤辉 余忆彬 王哲

　　摘 要：S着摄像测量技术在土木工程结构健康监测领域的应用逐渐增多，摄像测量技术的长期全天候工作性能受到越来越多的关注。为探究摄像测量技术的主要误差源，提出一种基于盲源分离（Blind Source Separation， BSS）的误差源分析新方法：为了构建多通道信号作为盲源分离模型的输入信号，采用集合经验模态分解法（Ensemble Empirical Mode Decomposition， EEMD）拓展观测信号通道;采用快速独立分量分析算法（Fast Independent Component Analysis， FastICA）对输入信号进行盲分离，得到FastICA分量;分析各分量与温度、光照等环境因素的相关性，探究主分量对应的误差源;利用分离算法所得的混合矩阵逆变换，计算各误差源分量的占比，确定摄像测量的主误差源。进行长期摄像测量误差源探究试验，通过盲源分离算法分析长期摄像测量误差数据，结果表明，该算法具有良好的分离效果，可有效分离提取各误差源所致位移误差分量，在长期摄像测量中，温度为主要误差源。
　　关键词：摄像测量;盲源分离;集合经验模态分解;误差源探究
　　中图分类号：TU196.4 文献标志码：A 文章编号：2096-6717（2022）03-0020-09
　　收稿日期：2021-05-31
　　基金项目：浙江省基础公益研究计划（LGF21E080010）;国家自然科学基金（51578424）
　　作者简介：周华飞（1978- ），男，博士，教授，主要从事结构健康监测研究，E-mail：mailtofei@zjut.edu.cn。
　　Received：2021-05-31
　　Foundation items：Zhejiang Provincial Natural Science Foundation of China （No. LGF21E080010）; National Natural Science Foundation of China （No. 51578424）
　　Author brief：ZHOU Huafei（1978- ）， PhD， professor， main research interest： structural health monitoring， E-mail：mailtofei@zjut.edu.cn.
　　Exploration of error sources of vision displacement monitoring technique by EEMD-FastICA algorithms
　　ZHOU Huafei， JIANG Xianhui1， YU Yibin2， WANG Zhe1
　　（1. School of Civil Engineering， Zhejiang University of Technology， Hangzhou 310013， P.R. China;2. Architecture and Civil Engineering， Wenzhou University， Wenzhou 325006， Zhejiang， P.R. China）
　　Abstract： With the increasing application of the vision measurement technique in the civil engineering structure health monitoring，more attention has been paid to the long-term all-weather performance of vision measurement.To explore the main error source of vision measurement technique，a new error source analysis method based on Blind Source Separation （BSS） is proposed： First， in order to construct the multi-channel signals as the input signals of the blind source separation model， Ensemble Empirical Mode Decomposition （EEMD） was used to expand the observation signal channels; then， Fast Independent Component Analysis （FastICA） algorithm was used to separate the input signals， to obtain the FastICA components; next，the correlation between each component and environmental factors such as temperature， light irradiation， etc.， was analyzed to explore the error source corresponding to the principal component; finally，by using the inverse transformation of the mixed matrix obtained by the separation algorithm， the proportion of the specified error source components was calculated and the main error source of the camera measurement was determined. The error data of long-term vision measurement were analyzed by blind source separation algorithm. The results show that this algorithm has good separation effect and can effectively separate and extract the displacement error components caused by each error source. In long-term vision measurement， temperature is the primary error source.
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