Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model

HU Chang-yu; CHEN Chun-feng; YI Wen-yi; DONG Yu-chen; LI Hui; WANG Ling

doi:10.12263/DZXB.20221326

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Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model

PAPERS | 更新时间：2026-04-10

- Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model
- ACTA ELECTRONICA SINICA Vol. 52, Issue 1, Pages: 170-180(2024)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(61661018);Shuang Chuang Ph. D Award(JSSCBS20210863;JSSCBS20210867);The General Program of Basic Science (Natural Science) Foundation of the Jiangsu Higher Education Institutions of China(22KJB140015);“Light of Taihu” Key Technologies R & D Program of Innovation and Entrepreneurship Foundation of Wuxi(K20221043;K20221049)
- DOI：10.12263/DZXB.20221326
  CLC： TN95;
- Received：18 November 2022，
  
  Revised：2023-05-06，
  
  Published：25 January 2024
- 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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