海军航空大学学报2024，Vol.39Issue(5)：603-614,12.DOI:10.7682/j.issn.2097-1427.2024.05.010
基于深度增强IST网络的ISAR稀疏成像
ISAR Sparse Imaging Based on Deep Augmented IST Network
摘要
Abstract
Addressing the issues of parameter sensitivity and slow convergence in traditional Inverse Synthetic Aperture Radar(ISAR)sparse imaging algorithms,inspired by the adaptive parameter learning mechanism of convolutional neural networks and combining the physical interpretability of model-driven networks,a new ISAR sparse imaging framework known as the Deep Augmented-Iterative Shrinkage Thresholding(DA-IST)network is proposed.Firstly,the DA-IST net-work maps the iterative steps of the Iterative Shrinkage Thresholding Algorithm(ISTA)into the hidden layers,which not only improves interpretability but also enables learning optimal parameters during training.Secondly,the network takes into account neglected high-frequency components during modeling,enhancing reconstruction performance.Additionally,to improve the network's robustness,nonlinear convolutional layers are employed to replace linear sparse transformations.Experimental results demonstrate that,compared to traditional model-driven algorithms,the DA-IST network eliminates the need for manual parameter tuning,exhibits faster convergence,produces higher-quality imaging,and possesses better generalization capabilities for data with significant feature differences.关键词
逆合成孔径雷达/稀疏成像/模型驱动网络/深度学习Key words
inverse synthetic aperture radar/sparse imaging/model driven network/deep learning分类
信息技术与安全科学引用本文复制引用
潘之梁,户盼鹤,陈凌峰,苏晓龙,刘振..基于深度增强IST网络的ISAR稀疏成像[J].海军航空大学学报,2024,39(5):603-614,12.基金项目
国家重点研发计划(2021YFB3100800) （2021YFB3100800）
国家自然科学基金(62022091、61921001) （62022091、61921001）
国防科技大学青年自主创新科学基金(ZK21-14、ZK23-18) （ZK21-14、ZK23-18）
