基于关键部件特征关联的ISAR空间目标多角度识别方法

袁浩轩; 张云; 黄艳堃; 田金; 孔维民; 陈李田

doi:10.12263/DZXB.20240860

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基于关键部件特征关联的ISAR空间目标多角度识别方法

学术论文 | 更新时间：2025-12-10

- 基于关键部件特征关联的ISAR空间目标多角度识别方法
- Multi-Angle ISAR Recognition Method for Space Targets Based on Feature Correlation of Key Components
- 电子学报 2025年53卷第7期页码：2401-2417
- 作者机构：
  
  哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "袁浩轩男，1997年生，黑龙江哈尔滨人.2024年毕业于哈尔滨工业大学信息与通信工程专业，现为哈尔滨工业大学副研究员.主要研究方向为雷达信号智能处理和深度学习识别框架.E-mail: hxyuan@hit.edu.cn" ]
  [ "张云女，1975年生，黑龙江虎林人.现为哈尔滨工业大学教授、博士生导师.主要研究方向为雷达信号处理、SAR成像、机器学习和遥感模式分析.E-mail: zhangyunhit@hit.edu.cn" ]
- 基金信息：
  
  中国博士后科学基金(2023M734272);国家自然科学基金(62371170;62201612)
- DOI：10.12263/DZXB.20240860
  中图分类号： TP751
- 收稿：2024-09-20，
  
  修回：2025-05-27，
  
  纸质出版：2025-07-25
- 稿件说明：
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袁浩轩, 张云, 黄艳堃, 等. 基于关键部件特征关联的ISAR空间目标多角度识别方法[J]. 电子学报, 2025, 53(07): 2401-2417.

YUAN Hao-xuan, ZHANG Yun, HUANG Yan-kun, et al. Multi-Angle ISAR Recognition Method for Space Targets Based on Feature Correlation of Key Components[J]. Acta Electronica Sinica, 2025, 53(07): 2401-2417.
袁浩轩, 张云, 黄艳堃, 等. 基于关键部件特征关联的ISAR空间目标多角度识别方法[J]. 电子学报, 2025, 53(07): 2401-2417. DOI：10.12263/DZXB.20240860

YUAN Hao-xuan, ZHANG Yun, HUANG Yan-kun, et al. Multi-Angle ISAR Recognition Method for Space Targets Based on Feature Correlation of Key Components[J]. Acta Electronica Sinica, 2025, 53(07): 2401-2417. DOI：10.12263/DZXB.20240860

摘要

在空间态势感知任务中常存在绕飞机动航天器，其持续的自旋、自旋轴变化和绕飞机动矢量构成了三维转动，导致相邻逆合成孔径雷达（Inverse Synthetic Aperture Radar，ISAR）图像帧间散射特性差异较大，难以识别.为此本文提出基于关键部件特征关联的ISAR空间目标多角度识别模型，构建基于自监督学习策略的对比学习模块，减少成像和目标姿态参数变化对图像识别的影响；构建关键部件特征关联模块，利用图信息推理方法挖掘图像关键部件之间的局部关联信息；构建复数域Transformer层，提取图像区域块之间的全局上下文特征，并通过特征融合实现对目标的有效表达.基于实测雷达数据的实验结果表明所提方法可显著提升多角度识别效果，在相同数据量识别条件下，与现有目标识别方法相比识别率提高了5.58%，验证了对目标多角度识别的性能.

Abstract

The geosynchronous space situational awareness program (GSSAP) satellite of the USA has repeatedly orbited and detected our satellites in recent years

which is a great threat. For this kind of orbiting maneuvering spacecraft

its continuous spin

spin axis change and orbiting motion vector constitute a three-dimensional rotation

and the scattering characteristics between adjacent inverse synthetic aperture radar (ISAR) image frames are greatly different

making it difficult to recognize it. To this end

this paper proposes a multi-angle ISAR recognition model for space targets based on feature correlation of key components. A contrast learning module based on self-supervised learning strategy is constructed to reduce the impact of parameter changes in imaging and target attitude on image recognition. A key component feature correlation module is constructed to mine local correlation information between key components of the images using graph information reasoning methods. Finally

a complex-valued transformer layer extracts global contextual features between image blocks and achieves effective expression of the target through feature fusion. Experimental results based on real radar data show that the proposed method can significantly improve the effect of multi-angle recognition. Under the same recognition condition of data volume

the recognition rate is increased by 5.58% compared with the existing recognition method

verifying the performance of multi-angle recognition.

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references

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