基于多域表征对齐融合的雷达目标无监督异常检测方法

张博安; 李坤城; 田隆; 陈文超; 刘西洋; 方明; 陈渤

doi:10.12263/DZXB.20251146

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基于多域表征对齐融合的雷达目标无监督异常检测方法

学术论文 | 更新时间：2026-05-11

- 基于多域表征对齐融合的雷达目标无监督异常检测方法
- Radar Target Unsupervised Anomaly Detection with Multi-Domain Representation Alignment and Fusion
- 电子学报 2026年54卷第2期页码：487-506
- 作者机构：
  
  1.西安电子科技大学计算机科学与技术学院，陕西西安 710071
  2.西安电子科技大学电子工程学院，陕西西安 710071
  3.西安电子科技大学雷达信号处理全国重点实验室，陕西西安 710071
  4.上海航天电子通讯设备研究所，上海 201109
- 作者简介：
  
  [ "张博安男，1999年出生于陕西省咸阳市。现为西安电子科技大学计算机科学与技术学院博士研究生。主要研究方向为复杂动态场景三维重建研究和异常检测方法研究。E-mail: boan@stu.xidian.edu.cn" ]
  [ "李坤城男，2000年出生于天津市。现为西安电子科技大学计算机科学与技术学院硕士研究生。主要研究方向为时间序列预测分析研究与雷达目标异常检测研究。E-mail: kcli@stu.xidian.edu.cn" ]
  [ "田隆男，1992年出生于陕西省西安市。现为西安电子科技大学计算机科学与技术学院副教授。主要研究方向为稀疏样本表示学习方法研究和智能信号处理方法研究。E-mail: tianlong@xidian.edu.cn" ]
  [ "陈文超男，1992年出生于山东省济宁市。现为西安电子科技大学电子工程学院副教授。主要研究方向为统计机器学习、雷达目标识别与检测、大规模数据处理以及深度学习。中国电子学会会员编号：E190045537M。E-mail: chenwenchao@xidian.edu.cn" ]
  [ "刘西洋男，1970年10月出生于陕西省延安市。现为西安电子科技大学计算机科学与技术学院教授。主要研究方向为工业智能以及医学人工智能等。E-mail: xyliu@xidian.edu.cn" ]
  [ "方明男，1987年出生于浙江省台州市。现为上海航天电子技术研究所高级工程师。主要研究方向为新体制雷达及雷达信号处理。E-mail: zj02065119@163.com" ]
  [ "陈渤男，1979年出生于河南省郑州市。现为西安电子科技大学电子工程学院教授。主要研究方向为雷达信号处理及在雷达目标检测识别方面的应用。中国电子学会会员编号：E190015313M。E-mail: bchen@mail.xidian.edu.cn" ]
- 基金信息：
  
  电磁空间安全全国重点实验室基金(NKYZX110320250200781);雷达信号处理全国重点实验室基金(KGJ202401);国家自然科学基金(82172860)
- DOI：10.12263/DZXB.20251146
  中图分类号： TN956;TN957.52
- 收稿：2026-01-22，
  
  录用：2026-02-24，
  
  纸质出版：2026-02-25
- 稿件说明：
移动端阅览
张博安, 李坤城, 田隆, 等. 基于多域表征对齐融合的雷达目标无监督异常检测方法[J]. 电子学报, 2026, 54(02): 487-506.

ZHANG Bo’an, LI Kuncheng, TIAN Long, et al. Radar Target Unsupervised Anomaly Detection with Multi-Domain Representation Alignment and Fusion[J]. Acta Electronica Sinica, 2026, 54(02): 487-506.
张博安, 李坤城, 田隆, 等. 基于多域表征对齐融合的雷达目标无监督异常检测方法[J]. 电子学报, 2026, 54(02): 487-506. DOI：10.12263/DZXB.20251146

ZHANG Bo’an, LI Kuncheng, TIAN Long, et al. Radar Target Unsupervised Anomaly Detection with Multi-Domain Representation Alignment and Fusion[J]. Acta Electronica Sinica, 2026, 54(02): 487-506. DOI：10.12263/DZXB.20251146

摘要

随着精确制导武器的演进与雷达探测技术的进步，基于雷达回波变化实现准确高效的目标毁伤评估，已成为现代战争中的关键挑战。该任务不仅涉及判断目标是否被击中，还需检测命中部位，其结果既是衡量攻防对抗效能的重要依据，也对战术策略与武器系统的优化具有指导意义。本文针对空空作战场景下的飞机目标，系统研究其毁伤评估方法，并重点攻克以下三个关键难点：第一，在真实战场环境中获取打击前后雷达回波数据极为困难，如何通过仿真构建毁伤场景并生成可信的雷达回波数据，是本研究需解决的基础问题；第二，毁伤部位及其形态具有高度随机性与不确定性，导致难以构建完备的毁伤特征库，限制了有监督检测方法的适用性；第三，为满足实时性需求，常采用一维距离像（High Resolution Range Profile，HRRP）进行目标状态监测，但其相较于逆合成孔径雷达图像（Inverse Synthetic Aperture Radar，ISAR），损失了大量稳定的结构特征，增加了毁伤本质特征提取的难度。针对上述挑战，本文提出一种基于多域表征对齐融合的雷达目标无监督异常检测方法，用于实现被打击飞机毁伤效果的准确高效评估。具体而言，针对毁伤场景构建与雷达回波数据生成困难的问题，提出基于Unity 3D的毁伤场景模拟方法，并结合雷达点散射中心模型生成目标回波数据；针对毁伤特征库不完备导致有监督信息难以利用的问题，构建基于正常信号重建的无监督异常检测框架，并引入自注意力机制设计“恒等映射”对消模块，以抑制模型退化，提升毁伤识别能力；针对目标结构信息有限导致本征特征提取困难的问题，提出一种多域表征对齐融合的无监督正则方法，通过引入ISAR像特征以增强HRRP中的结构信息表达，并设计基于格拉姆矩阵的体积度量函数，实现HRRP与ISAR像之间的稳健域对齐，从而增强毁伤本征特征的挖掘能力。从贝叶斯参数优化的视角来看，正常信号重建为模型参数学习提供了可优化的似然函数，而多域表征对齐融合则对应为可优化的KL散度项，二者共同构成统一的理论框架。本文在自研的目标毁伤仿真数据集上对所提方法进行了验证。实验结果表明，在无监督信号及目标ISAR像的测试条件下，该方法能够仅基于正常状态下的HRRP数据，有效挖掘出具有判别力的毁伤特征，实现正常与毁伤状态的准确区分。进一步地，通过迁移并融合目标ISAR像中的结构信息，模型在毁伤评估任务中的受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic curve，AUROC）相较于仅使用HRRP的模型提升12.31个百分点，验证了所提方法具有良好的泛化能力与工程应用潜力。

Abstract

With the evolution of precision-guided munitions and advances in radar sensing technologies

achieving accurate and efficient battle-damage assessment (BDA) based on changes in radar echoes has become a critical challenge in modern warfare. This task not only involves determining whether a target has been hit

but also detecting the hit location; its outcomes serve as an important basis for measuring the effectiveness of offense-defense engagements and provide guidance for optimizing tactics and weapon systems. This paper systematically investigates methods for damage assessment of aircraft targets in air-to-air combat scenarios and focuses on overcoming the following three key difficulties. First

obtaining radar echo data before and after strikes in real battlefield environments is extremely difficult; how to construct damage scenarios via simulation and generate credible radar echo data is a fundamental problem this study must solve. Second

the hit locations and their morphologies exhibit high randomness and uncertainty

making it difficult to build a comprehensive damage feature library and thereby limiting the applicability of supervised detection methods. Third

to meet real-time requirements

one-dimensional range profiles (high resolution range profile

HRRP) are commonly used for target state monitoring; however

compared with inverse synthetic aperture radar (ISAR) images

HRRP lacks many stable structural features

increasing the difficulty of extracting intrinsic damage features. To address these challenges

this paper proposes an unsupervised anomaly-detection method for radar targets based on multi-domain representation alignment and fusion

aimed at achieving accurate and efficient assessment of damage effects on struck aircraft. Specifically

to tackle the difficulty of constructing damage scenarios and generating radar echo data

we propose a damage-scene simulation method based on Unity 3D

and generate target echo data by combining it with a radar point-scatterer-center model. To address the problem that an incomplete damage feature library makes supervised information hard to utilize

we construct an unsupervised anomaly-detection framework based on reconstruction of normal signals

and introduce a self-attention mechanism to design an “identity-mapping” cancellation module to suppress model degeneration and enhance damage recognition capability. To tackle the difficulty of extracting intrinsic features due to limited target structural information

we propose an unsupervised regularization method of multi-domain representation alignment and fusion: by introducing ISAR image features to augment structural information in HRRP

and by designing a volume-metric function based on the Gram matrix to achieve robust domain alignment between HRRP and ISAR images

thereby enhancing the mining of intrinsic damage features. From the perspective of Bayesian parameter optimization

reconstruction of normal signals provides an optimizable likelihood function for model parameter learning

while multi-domain representation alignment and fusion correspond to an optimizable KL-divergence term; together they form a unified theoretical framework. We validate the proposed method on a self-developed simulated dataset of target damage. Experimental results indicate that

under test conditions with unsupervised signals and target ISAR images

the method can

relying solely on HRRP data from the normal state

effectively discover discriminative damage features and accurately distinguish between normal and damaged states. Furthermore

by transferring and fusing structural information from target ISAR images

the model’s area under the receiver operating characteristic curve (AUROC) on the damage-assessment task improves by 12.31 percentage points compared with the HRRP-only model. The above results validate that the proposed method possesses strong generalization capability and engineering application potential.

关键词

Keywords

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