A Sparse Aperture ISAR Imaging and Autofocusing Method Based on Meta-Learning Framework

被引:5
作者
Li, Ruize [1 ]
Zhang, Shuanghui [1 ]
Liu, Yongxiang [1 ]
Li, Xiang [1 ]
机构
[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
Compressed sensing (CS); deep unfolding; inverse synthetic aperture radar (ISAR); meta-learning; MANEUVERING TARGETS; ADMM;
D O I
10.1109/TAP.2024.3361664
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The cross-range resolution of inverse synthetic aperture radar (ISAR) images is influenced by undersampled data under the sparse aperture (SA) condition. Recently, learning-based methods have been applied to SA-ISAR imaging and have achieved impressive performance. Learning-based methods can achieve satisfactory results by training on large datasets. However, these methods may fail to reconstruct high-quality images in practical applications due to training data limitations. In this article, we consider this problem within a meta-learning framework. In this framework, the SA-ISAR imaging network is trained by a learnable optimizer instead of a fixed stochastic gradient descent (SGD) optimizer. A fully connected network is designed as an optimizer for imaging network training; this network is also called a meta-learner. The whole training procedure in the proposed framework is divided into two parts. In the first part, a suitable meta-learner is trained. In the second part, the well-trained meta-learner is applied to train the ISAR imaging network. In this article, our previously proposed complex-valued alternating direction method of multipliers network (CV-ADMMN) is trained within this framework; this approach is called Meta-CV-ADMMN. The experimental results show that the proposed training framework can improve the imaging performance and data adaptability of CV-ADMMN, especially when the training data are limited.
引用
收藏
页码:3529 / 3544
页数:16
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