Multi-Aspect SAR Feature Extraction Based on Compressive Sensing

ZHOU Han-fei; LI Yu; SU Yi

doi:10.3969/j.issn.0372-2112.2013.03.021

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Multi-Aspect SAR Feature Extraction Based on Compressive Sensing

更新时间：2025-07-16

- Multi-Aspect SAR Feature Extraction Based on Compressive Sensing
- Acta Electronica Sinica Vol. 41, Issue 3, Pages: 543-548(2013)
- 作者机构：
  
  国防科学技术大学电子科学与工程学院,湖南,长沙,410073
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.60972120, No.61171135)
- DOI：10.3969/j.issn.0372-2112.2013.03.021
  CLC： TN957
- Published：2013
- 稿件说明：
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ZHOU Han-fei, LI Yu, SU Yi. Multi-Aspect SAR Feature Extraction Based on Compressive Sensing[J]. Acta Electronica Sinica, 2013, 41(3): 543-548.
DOI：

ZHOU Han-fei, LI Yu, SU Yi. Multi-Aspect SAR Feature Extraction Based on Compressive Sensing[J]. Acta Electronica Sinica, 2013, 41(3): 543-548. DOI： 10.3969/j.issn.0372-2112.2013.03.021.

摘要

为提高目标属性散射中心参数估计的精度和鲁棒性

利用多角度SAR数据作为输入

将参数估计问题转化为稀疏向量重构问题

使用分步估计算法提高计算效率

从而实现多角度SAR特征提取.研究内容包括两方面

一是论证多角度SAR的角度和频率分集特性对字典矩阵性能的改善.另外

为提高算法效率

本文提出分步参数估计算法.首先用理想点目标模型得到初步估计的图像表示

然后通过图像分割和能量中心计算估计模型阶次、位置和散射类型

最后以初步估计为先验信息重新构造字典矩阵

得到最终估计.实验验证了算法鲁棒性以及分辨率的改善.

Abstract

In order to improve the precision and robustness of parameter estimation

the multi-aspect SAR data is applied to estimate model parameter of attributed scattering center.We consider the parameter estimation as a sparse signal reconstruction problem

and propose parameter-sequential algorithm to relieve the computational complexity.Two factors are studied.First

the aspect and frequency diversity can improve the performance of dictionary matrix.Second

in order to reduce the algorithm complexity

the parameter estimation procedure is realized sequentially.The initial imagery is reconstructed by dictionary matrix which is built up by the ideal point scattering model.The model order

location and type of scattering center are established primarily by energy segment of initial imagery.Then all parameters are estimated over again based on the dictionary matrix which is built up by the prior estimation.The feasibility and robustness of algorithm is validated by numeric simulation.

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