.png){kind=icon}
返回
Efficient ISAR Phase Autofocus Based on Eigenvalue Decomposition
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2017.2746758.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Phase autofocus is a key step in translational motion compensation for inverse synthetic aperture radar. From the eigenvalue decomposition (EVD) of the covariance matrix generated by the aligned range-compressed signal, eigenvectors can be obtained for effective phase autofocus. However, as the number of pulse samples is increased to improve the cross-range resolution, the high computational complexity of the EVD may become burdensome. To address this problem, we propose a novel EVD-based method in this letter. When the number of range units is larger than the number of pulse samples, the conventional method is used. Otherwise, the transpose of the envelope-aligned data matrix is used to generate a lower dimensional covariance matrix and to perform successive autofocus processing. Since many real targets exist in limited range units, a one-or two-order-higher computational efficiency can be obtained in some typical scenarios with the proposed method, compared with existing EVD-based approaches. Furthermore, the equivalence between the above two methods has been proven in this letter. Finally, the results for real measured data are provided to demonstrate the effectiveness of the proposed method.
Keyword:
Computational efficiency
eigenvalue decomposition (EVD)
inverse synthetic aperture radar (ISAR)
phase autofocus
translational motion compensation (TMC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
lp-Based complex approximate message passing with application to sparse stepped frequency radar.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
查看更多内容