现代雷达2026，Vol.48Issue(5)：40-49,10.DOI:10.16592/j.cnki.1004-7859.20240909002
基于跳跃卷积神经网络的ISAR稀疏成像方法
ISAR Sparse Imaging Method Based on the Skip Convolutional Neural Network
摘要
Abstract
Convolutional neural network(CNN)provides a new solution for inverse synthetic aperture radar(ISAR)imaging.However,there are large semantic feature gaps between the abstract feature representations of the input ISAR data extracted by ex-isting imaging CNNs,limiting the improvement of the imaging quality of CNN-based ISAR sparse imaging methods.In order to im-prove the imaging quality of CNN-based ISAR imaging,a skip convolutional connection structure is designed to optimize the ima-ging CNN architecture,and a skip convolutional neural network(S-CNN)is constructed;on this basis,an ISAR sparse imaging method based on S-CNN is proposed.The experimental results show that,compared with the existing ISAR sparse imaging methods of the same type,the proposed method achieves better imaging results,with an average improvement of 6.71％in quantitative indi-cators such as image entropy and target clutter ratio.关键词
逆合成孔径雷达/稀疏成像/深度学习/跳跃卷积神经网络/跳跃卷积连接Key words
inverse synthetic aperture radar(ISAR)/sparse imaging/deep learning/skip convolution neural network(S-CNN)/skip convolutional connection分类
信息技术与安全科学引用本文复制引用
蔡松,胡长雨,向天舜,田凤..基于跳跃卷积神经网络的ISAR稀疏成像方法[J].现代雷达,2026,48(5):40-49,10.基金项目
江苏省高等学校基础科学(自然科学)研究面上资助项目(22KJB140015,23KJB510035) （自然科学）
无锡市"太湖之光"科技攻关基础研究资助项目(K20221043,K20221049) （K20221043,K20221049）
无锡学院人才科研启动经费资助项目(2023r014) （2023r014）
南京航空航天大学微波光子与雷达成像教育部重点实验室开放基金资助项目(NJ20230006) （NJ20230006）
