摘要
鉴于稀疏ISAR成像方法的成像质量受到待成像场景的稀疏表示不准确的限制,该文将字典学习(DL)技术引入到ISAR稀疏成像中,以提升目标成像质量。该文给出基于离线DL和在线DL两种ISAR稀疏成像方法。前者通过已有同类目标ISAR图像进行学习,获得更优稀疏表示,后者在成像过程中从现有数据中通过优化获得稀疏表示。仿真和实测ISAR数据成像结果表明,结合离线DL和在线DL的成像方法均可获得比现有方法更优的成像结果,离线DL成像优于在线DL成像,而且前者计算效率优于后者。
In view of the imaging quality of sparse ISAR imaging methods is limited by the inaccurate sparse representation of the scene to be imaged,the Dictionary Learning (DL) technique is introduced into ISAR sparse imaging to get better sparse representation of the scene.An off-line DL based imaging method and an on-line DL based imaging method are proposed.The off-line DL imaging method can obtain a better sparse representation via a dictionary learned from the available ISAR images.The on-line DL imaging method can obtain the sparse representation from the data currently considered by jointly optimizing the imaging and DL processes.The results of both simulated and real ISAR data show that the on-line DL imaging method and the off-line dictionary imaging method are both able to better sparsely represent the target scene leading to better imaging results.The off-line DL based imaging method works better than the on-line DL based imaging method with respect to both imaging quality and computational efficiency.
作者
胡长雨
汪玲
朱栋强
HU Changyu;WANG Ling;ZHU Dongqiang(Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2019年第7期1735-1742,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61871217)
江苏省研究生科研与实践创新计划(KYCX18_0291)~~
关键词
逆合成孔径雷达
稀疏成像
稀疏表示
字典学习
压缩感知
Inverse Synthetic Aperture Radar (ISAR)
Sparse imaging
Sparse representation
Dictionary Learning(DL)
Compressive Sensing (CS)