结合最近邻图模型的稀疏ISAR成像方法

胡长雨; 陈春风; 易文忆; 董宇宸; 李晖; 汪玲

doi:10.12263/DZXB.20221326

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结合最近邻图模型的稀疏ISAR成像方法

学术论文 | 更新时间：2026-04-10

- 结合最近邻图模型的稀疏ISAR成像方法
- Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model
- 电子学报 2024年52卷第1期页码：170-180
- 作者机构：
  
  1.无锡学院电子信息工程学院，江苏无锡 214000
  2.南京航空航天大学雷达成像与微波光子教育部重点实验室，江苏南京 210016
  3.无锡学院自动化学院，江苏无锡 214000
- 作者简介：
  
  [ "胡长雨 1989年出生于江西抚州.现为无锡学院讲师.主要研究方向为智能雷达成像." ]
  [ "陈春风 1982年出生于黑龙江哈尔滨.现为南京航空航天大学博士研究生.主要研究方向为雷达成像与动目标检测." ]
  [ "易文忆 2000年出生于江西九江.现为无锡学院自动化学院本科生." ]
  [ "董宇宸 2003年出生于重庆.现为无锡学院电子信息工程学院本科生." ]
  [ "李晖 1977年出生于河南叶县.博士.现为无锡学院教授、硕士生导师.主要研究方向为智能信息处理、网络信息处理、网络安全、多目标优化理论与应用." ]
  [ "汪玲 1977年出生于河南开封.现为南京航空航天大学教授、博士生导师，德国洪堡学者.主要研究方向为逆合成孔径雷达成像." ]
- 基金信息：
  
  国家自然科学基金(61661018);江苏省双创博士人才项目(JSSCBS20210863;JSSCBS20210867);江苏省高等学校基础科学（自然科学）研究面上项目(22KJB140015);无锡市创新创业资金“太湖之光”科技攻关计划（基础研究）项目(K20221043;K20221049)
- DOI：10.12263/DZXB.20221326
  中图分类号： TN95;
- 收稿：2022-11-18，
  
  修回：2023-05-06，
  
  纸质出版：2024-01-25
- 稿件说明：
移动端阅览
胡长雨,陈春风,易文忆等.结合最近邻图模型的稀疏ISAR成像方法[J].电子学报,2024,52(01):170-180.

HU Chang-yu,CHEN Chun-feng,YI Wen-yi,et al.Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model[J].ACTA ELECTRONICA SINICA,2024,52(01):170-180.
胡长雨,陈春风,易文忆等.结合最近邻图模型的稀疏ISAR成像方法[J].电子学报,2024,52(01):170-180. DOI： 10.12263/DZXB.20221326.

HU Chang-yu,CHEN Chun-feng,YI Wen-yi,et al.Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model[J].ACTA ELECTRONICA SINICA,2024,52(01):170-180. DOI： 10.12263/DZXB.20221326.

摘要

逆合成孔径雷达（Inverse Synthetic Aperture Radar，ISAR）稀疏成像方法可提供图像对比度高、旁瓣干扰少的成像结果.稀疏成像以场景或目标散射率分布具有稀疏性为前提，待成像目标场景的稀疏特性决定了最终成像质量.ISAR目标场景的自然稀疏特性着重刻画点状特征，变换域稀疏表示可增强目标图像的纹理等通用特征.通过学习获得的稀疏变换字典，可自适应于待成像的ISAR目标场景，找到面向ISAR目标图像块的特有稀疏表示.但是，图像块的特有稀疏表示中忽略了待成像目标场景中目标的几何特征信息.最近邻图模型可建立给定数据的几何特征描述算子，刻画出给定数据的几何特征信息.本文利用最近邻图模型来刻画待成像目标场景中目标的几何特征信息，并映射到待成像目标场景的特有稀疏表示中；提出结合最近邻图模型的ISAR稀疏成像方法，用于不同类别实测ISAR数据成像.相比已有的ISAR稀疏成像方法，所提成像方法可获得目标轮廓更清晰的成像结果，成像所需时间平均减少10.4%.

Abstract

Inverse synthetic aperture radar (ISAR) sparse imaging methods can provide the imaging results with high image contrast and less sidelobe interference. The premise of sparse imaging is that the scatterers distribution of the scene or target to be imaged is sparse

which means that the final imaging quality is determined by the sparse feature of the target or scene to be imaged. The natural sparsity of ISAR target scenes emphasize point-like features

and the sparse representations of to be imaged target scene in the transform domains can enhance general features (e.g.

texture or contour features) of the target. The well learned sparse transformation dictionaries can adapt to the to be imaged target scenes and find their unique sparse representations. However

the image patches oriented sparse representations ignore the geometric feature of target to be imaged. The nearest neighbor graph model is able to establish the geometric feature description operator of the given data

which can be used for describing the geometric feature information of the given data. In this paper

we introduce the nearest neighbor graph model (NNGM) into ISAR sparse imaging to express the geometric feature of the to be imaged target. The NNGM of the to be imaged target is then used as the regularization term and mapped to ISAR sparse imaging model. We propose an ISAR sparse imaging method combined with the NNGM for the imaging of different types of real ISAR data. Compared with the existing ISAR sparse imaging methods

the proposed imaging method can provide the imaging result with clearer contour

and the imaging time is reduced by 10.4% on average.

关键词

Keywords

references

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