基于协同注意力机制的跨模态特征融合干扰辨识方法

董阳阳; 杨孔婧; 冯家琛

doi:10.12382/bgxb.2025.0725

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基于协同注意力机制的跨模态特征融合干扰辨识方法

更新时间：2026-05-06

- 基于协同注意力机制的跨模态特征融合干扰辨识方法
- Co-attention-based Mechanism Cross-modal Feature Fusion for Jamming Signal Identification
- 兵工学报 2026年47卷第4期页码：250725
- 作者机构：
  
  西安电子科技大学电子工程学院，陕西西安 710071
- 作者简介：
  
  通信作者邮箱：yydong@xidian.edu.cn
- 基金信息：
- DOI：10.12382/bgxb.2025.0725
  中图分类号： TN973
- 收稿：2025-08-08，
  
  网络首发：2026-01-22，
  
  纸质出版：2026-04
- 稿件说明：
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董阳阳, 杨孔婧, 冯家琛. 基于协同注意力机制的跨模态特征融合干扰辨识方法[J]. 兵工学报, 2026,47(4):250725.

DONG Yangyang, YANG Kongjing, FENG Jiachen. Co-attention-based Mechanism Cross-modal Feature Fusion for Jamming Signal Identification[J]. Acta Armamentarii, 2026, 47(4): 250725.
董阳阳, 杨孔婧, 冯家琛. 基于协同注意力机制的跨模态特征融合干扰辨识方法[J]. 兵工学报, 2026,47(4):250725. DOI： 10.12382/bgxb.2025.0725.

DONG Yangyang, YANG Kongjing, FENG Jiachen. Co-attention-based Mechanism Cross-modal Feature Fusion for Jamming Signal Identification[J]. Acta Armamentarii, 2026, 47(4): 250725. DOI： 10.12382/bgxb.2025.0725.

摘要

针对现代雷达干扰策略自动化、样式多样化、参数精准化引起的干扰辨识效能下降问题，提出一种基于协同注意力机制的跨模态特征融合干扰辨识模型。该模型通过协同注意力机制融合干扰信号时频模态和距离多普勒模态的深度特征，充分挖掘出不同模态间的互补信息；同时结合迁移学习策略预训练残差神经网络，有效克服小样本训练数据限制，显著提升复杂干扰信号的辨识性能。仿真实验结果表明，所提方法在低干噪比和复杂干扰环境下仍保持较高的识别准确率，干噪比为-3dB时识别准确率不低于96%；在小样本训练条件下，干噪比为-3dB及以上时识别准确率不低于85%，验证了该方法的有效性和鲁棒性。

Abstract

To address the degradation in the jamming identification performance of radar caused by the automation

diversification and parameter precision of modern radar jamming strategies

a cross-modal feature fusion recognition model based on a collaborative attention mechanism is proposed. The deep features from the time-frequency modality and range-Doppler modality of jamming signals are effectively integrated through the collaborative attention mechanism

allowing the complementary information between different modalities to be fully exploited. Meanwhile

a residual neural network is pre-trained using a transfer learning strategy to effectively mitigate the limitations imposed by small-sample training data

leading to significant improvement in the identification performance for complex jamming signals. Simulated results demonstrate that the proposed method maintains a higher recognition accuracy under low jamming-to-noise ratio (JNR) and complex jamming scenarios. A recognition accuracy of no less than 96% is achieved at a JNR of -3dB. The recognition accuracy remains no less than 85% at JNR values of-3dB and above under small-sample training conditions

which validates the effectiveness and robustness of the proposed method.

关键词

Keywords

references

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