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一种三维变形监测方法:基于回归模型的光学变形监测与地面合成孔径雷达干涉测量相结合

A Three-Dimensional Deformation Monitoring Method: Combining Optical Deformation Monitoring Based on Regression Models and GB-SAR Interferometry.

作者信息

Cheng Yanbo, Mo Yuanhui, Huang Haifeng, Lai Tao

机构信息

School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen 518107, China.

出版信息

Sensors (Basel). 2024 Mar 8;24(6):1754. doi: 10.3390/s24061754.

DOI:10.3390/s24061754
PMID:38544017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10976168/
Abstract

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level.

摘要

本文介绍了一种用于量化地面目标三维变形的方法,并概述了相关过程。首先,利用地基合成孔径雷达监测目标的径向变形,光学设备监测视线垂直平面内的像素级变形。随后,建立回归模型,基于基本长度单位将像素级变形转换为二维变形,并将雷达变形监测数据与光学变形监测数据合并。最后,对融合后的数据进行变形处理,得到目标的综合三维变形轮廓。通过物理数据采集实验,获得了目标的综合三维变形,并与实际变形进行了比较。实验结果表明,该方法的相对误差小于10%,监测精度达到毫米级。

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本文引用的文献

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Sensors (Basel). 2020 Dec 8;20(24):7027. doi: 10.3390/s20247027.
2
Multi-camera and structured-light vision system (MSVS) for dynamic high-accuracy 3D measurements of railway tunnels.用于铁路隧道动态高精度三维测量的多相机与结构光视觉系统(MSVS)
Sensors (Basel). 2015 Apr 14;15(4):8664-84. doi: 10.3390/s150408664.