.png){kind=icon}
返回
High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2011.2151865.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.
Keyword:
Compressive sensing (CS)
inverse synthetic aperture radar (SAR) (ISAR)
radar imaging
sparse stepped-frequency waveform (SFW) (SSFW)
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:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
分享收藏Independent Evaluation of the Respective Predictive Values for High-Grade Prostate Cancer of Clinical Information and RNA Biomarkers after Upfront MRI and Image-Guided Biopsies.png)
Cancers
OAAI
CancersIF0
OAAI分享收藏Rat Polymerase β Binds Double-Stranded DNA Using Exclusively the 8-kDa Domain. Stoichiometries, Intrinsic Affinities, and Cooperativities,Biochemistry
BiochemistryIF0
PREAI
分享收藏分享收藏Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing基于改进压缩感知的低信噪比下逆合成孔径雷达成像分辨率增强
PREAI
分享收藏
查看更多内容