雷达科学与技术2024，Vol.22Issue(4)：427-433,453,8.DOI:10.3969/j.issn.1672-2337.2024.04.009
基于深度展开网络的SFGPR压缩感知成像方法
SFGPR Compressive Sensing Imaging Method Based on Deep Unfolding Network
摘要
Abstract
Aiming at the problems of sensitive parameter selection and low imaging accuracy in the traditional compressive sensing imaging method of stepped frequency ground penetrating radar(SFGPR),a SFGPR compressive sensing imaging method based on deep unfolding network is proposed.This method first maps the iterative process of the fast iterative shrinkage threshold algorithm to the deep network structure,and then adds the convolutional neural net-work module as the sparse representation of the imaging area and its inverse process.The parameters that need to be manually adjusted are set to learnable network parameters.Finally,the network is trained and tested using the down-sampling echo data after clutter suppression.The simulation and measured data processing results show that this method can improve the imaging accuracy of underground targets without manual adjusting parameters.关键词
深度展开网络/频率步进探地雷达/快速迭代收缩阈值算法/压缩感知Key words
deep unfolding network/stepped frequency ground penetrating radar/fast iterative shrinkage thres-hold algorithm/compressive sensing分类
信息技术与安全科学引用本文复制引用
孙延鹏,尹鑫戊,屈乐乐..基于深度展开网络的SFGPR压缩感知成像方法[J].雷达科学与技术,2024,22(4):427-433,453,8.基金项目
国家自然科学基金(No.61671310) （No.61671310）
航空科学基金(No.2019ZC054004) （No.2019ZC054004）
