基于ControlVAE的约束嵌入稠密时变阵列构建

孙文力; 原达; 姜新波

doi:10.12263/DZXB.20221144

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基于ControlVAE的约束嵌入稠密时变阵列构建

学术论文 | 更新时间：2026-04-10

- 基于ControlVAE的约束嵌入稠密时变阵列构建
- Construction of Dense Time-Varying Array with Constraint Embedded Based on ControlVAE
- 电子学报 2024年52卷第1期页码：354-363
- 作者机构：
  
  1.山东工商学院山东高校智能信息处理重点实验室，山东烟台 264005
  2.山东大学齐鲁交通学院，山东济南 250002
- 作者简介：
  
  [ "孙文力男，1998年4月生，山东烟台人.现为山东工商学院信息与工程技术学院硕士研究生.主要研究方向为探地雷达数据处理与应用、深度学习.E-mail: swlswenli@163.com" ]
  [ "原达男，1968年10月生，辽宁建昌人.现为山东高校智能信息处理重点实验室（山东工商学院）主任，教授.主要研究方向为探地雷达、可视化处理. E-mail: ydccec@126.com" ]
  [ "姜新波男，1987年2月生，山东烟台人.现为山东大学齐鲁交通学院助理研究员、硕士生导师. 主要研究方向为计算机视觉、人工智能.E-mail: xinbojiang@sdu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62072285);山东省重点研发计划(2019GGX101040)
- DOI：10.12263/DZXB.20221144
  中图分类号： TP751.1;
- 收稿：2022-10-11，
  
  修回：2023-05-25，
  
  纸质出版：2024-01-25
- 稿件说明：
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孙文力,原达,姜新波.基于ControlVAE的约束嵌入稠密时变阵列构建[J].电子学报,2024,52(01):354-363.

SUN Wen-li,YUAN Da,JIANG Xin-bo.Construction of Dense Time-Varying Array with Constraint Embedded Based on ControlVAE[J].ACTA ELECTRONICA SINICA,2024,52(01):354-363.
孙文力,原达,姜新波.基于ControlVAE的约束嵌入稠密时变阵列构建[J].电子学报,2024,52(01):354-363. DOI： 10.12263/DZXB.20221144.

SUN Wen-li,YUAN Da,JIANG Xin-bo.Construction of Dense Time-Varying Array with Constraint Embedded Based on ControlVAE[J].ACTA ELECTRONICA SINICA,2024,52(01):354-363. DOI： 10.12263/DZXB.20221144.

摘要

在对探地雷达（Ground Penetrating Radar，GPR）数据进行三维建模与可视化分析过程中，其所依赖的三维时变阵列通常是由一维数据道或二维阵列间接生成的.由于采集到的数据往往比较稀疏，且存在不规则偏移，需要进行稠密处理，以获得高密度三维时变阵列.本文提出了一种基于可控变分自编码（Controllable Variational AutoEncoder，ControlVAE）的约束嵌入稠密时变阵列构建方法.使用基于ControlVAE的时变数据重构网络，通过隐空间采样插值和深浅层特征信息的融合，生成接近真实分布的伪道数据来增加阵列密度.构建了基于尺度不变特征变换的数据配准模块，提取道间梯度特征与结构对称性特征，可以在时空域内完成数据配准.同时将浅层特征作为约束信息嵌入数据重构网络，以消除数据道偏移对阵列生成的影响.实验结果表明，本文方法仅使用单测线二维B-scan数据集即可重建稠密时变阵列，可以有效降低重构模型累积误差，提高面对复杂真实数据时的鲁棒性.

Abstract

In the 3D modeling and visualization analysis of ground penetrating radar (GPR) data

the 3D time-varying arrays they rely on are usually generated indirectly from 1D data channels or 2D arrays. Since these data are often sparse and irregularly shifted

dense processing is required to obtain high-density 3D time-varying arrays. This paper proposes a constrained embedding dense time-varying array construction method based on controllable variational autoencoder (ControlVAE). A time-varying data reconstruction network based on the ControlVAE is used to increase the array density by generating pseudo-channel data close to the natural distribution through the fusion of hidden space sampling interpolation and deep and shallow feature information. A data alignment module based on scale-invariant feature transformation is constructed to extract the inter-channel gradient features and structural symmetry features to complete the data alignment in the spatial domain. The shallow features are also embedded as constraint information in the data generation network to eliminate the influence of data channel offset on array generation. The experimental results show that the proposed method can efficiently reconstruct a time-varying array with a single scan direction using a two-dimensional B-scan data set. This method can effectively reduce the cumulative error of the reconstructed model and improve the robustness in the face of complex actual data.

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references

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