Novel Autofocusing Algorithm for ISAR Imaging Based on Sparse Constraint

XU Gang; ZHANG Lei; CHEN Qian-qian; XING Meng-dao

doi:10.3969/j.issn.0372-2112.2013.09.016

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Novel Autofocusing Algorithm for ISAR Imaging Based on Sparse Constraint

更新时间：2025-07-16

- Novel Autofocusing Algorithm for ISAR Imaging Based on Sparse Constraint
- Acta Electronica Sinica Vol. 41, Issue 9, Pages: 1772-1777(2013)
- 作者机构：
  
  西安电子科技大学雷达信号处理国防科技重点实验室,陕西,西安,710071
- 作者简介：
- 基金信息：
  
  Program supported by National Key Basic Research Development Plan (No.2010CB731903)
- DOI：10.3969/j.issn.0372-2112.2013.09.016
  CLC： TN958
- Published：2013
- 稿件说明：
移动端阅览
XU Gang, ZHANG Lei, CHEN Qian-qian, et al. Novel Autofocusing Algorithm for ISAR Imaging Based on Sparse Constraint[J]. Acta Electronica Sinica, 2013, 41(9): 1772-1777.
DOI：

XU Gang, ZHANG Lei, CHEN Qian-qian, et al. Novel Autofocusing Algorithm for ISAR Imaging Based on Sparse Constraint[J]. Acta Electronica Sinica, 2013, 41(9): 1772-1777. DOI： 10.3969/j.issn.0372-2112.2013.09.016.

摘要

本文提出了一种基于稀疏约束的ISAR方位自聚焦算法

能够应用于稀疏孔径ISAR成像中.该算法利用ISAR图像的稀疏特征建立最小1范数成像模型

并将相位误差作为模型误差.然后通过数值迭代的方式进行自适应相位误差估计

最终获得聚焦良好的ISAR图像.同时

成像代价函数的建立基于矩阵模型

有利于采用方位FFT和矩阵的Hardmard乘积操作进行快速求解.由于利用稀疏约束

该方法在低信噪比的条件下仍然能够取得良好的聚焦结果.基于仿真数据和实测数据的结果验证了本文算法的有效性.

Abstract

In this paper

a novel autofocusing algorithm of inversed synthetic aperture radar (ISAR) imaging based on sparse constraint is proposed

which can be applied in sparse aperture ISAR imaging.In the scheme

taking the phase errors as model errors

the proposed approach exploits the sparsity prior of ISAR image to construct the minimum 1-norm image formation.Then numerical method is adopted to realize adaptive phase error estimation while well-focused ISAR image can finally be obtained.Meanwhile

the objective function of ISAR imaging is established based on matrix model

which can be conveniently solved using fast Fourier transform (FFT) and matrix Hardmard multiplication.Due to the utilization of sparsity restriction

the proposed approach can still be capable of performing well even in the case of low signal-to-noise ratio (SNR).The experimental results using both simulated data and measured data confirm the validation of the proposal.

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