.png){kind=icon}
返回
Superresolution ISAR Imaging Based on Sparse Bayesian Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2013.2286402.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the exact sparse reconstruction, i.e., l(0)-norm constraint, is NP hard, l(1)-norm relaxation is widely used at the cost of performance degradation in the sparseness of the solution. The performance of existing CS-based ISAR imaging algorithms is sensitive to the regularized factor, which should be adjusted manually. This makes the existing algorithms inconvenient to be used in practice. It is well known that sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which is closely related to the CS. Furthermore, all the necessary parameters can be estimated using an efficient evidence maximization procedure in SBL, which retains a preferable property of the l(0)-norm diversity measure and can give more sparse solution. Motivated by that, a fully automated ISAR imaging algorithm based on SBL is proposed in this paper. Experimental results based on simulated and measured data show that the proposed algorithm keeps a better balance between the computation load and the sparsity of the reconstruction signal than the existing algorithms.
Keyword:
Compressive sensing (CS)
inverse synthetic aperture radar (SAR) (ISAR)
sparse Bayesian learning (SBL)
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
.png){kind=icon}
分享收藏Arsenic Removal from Natural Water Using Low Cost Granulated Adsorbents: A Review使用低成本颗粒状吸附剂从天然水中去除砷: 综述
OAAI
OAAI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