Volume 13 Issue 5
Sep.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(5): 1004-1018
GUO Wenjie, WU Zhenhua, CAO Yice, et al. Multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming[J]. Journal of Radars, 2024, 13(5): 1004–1018. doi: 10.12000/JR24129
Citation: GUO Wenjie, WU Zhenhua, CAO Yice, et al. Multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming[J]. Journal of Radars, 2024, 13(5): 1004–1018. doi: 10.12000/JR24129
shu

Multidomain Characteristic-guided Multimodal Contrastive Recognition Method for Active Radar Jamming

DOI: 10.12000/JR24129 CSTR: 32380.14.JR24129
  • 1.

    School of Electronic and Information Engineering, Anhui University, Hefei 230601, China

  • 2.

    The 38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China

  • 3.

    National Key Laboratory of Space Integrated Information System, Beijing 100094, China

  • 4.

    School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds:  The National Natural Science Foundation of China (62201007, 62401007), The China Postdoctoral Science Foundation (2020M681992), Anhui Provincial Natural Science Foundation (2308085QF199)
More Information
  • Corresponding author: WU Zhenhua, zhwu@ahu.edu.cn; CAO Yice, yccao@ahu.edu.cn
  • Received Date: 2024-06-21
  • Rev Recd Date: 2024-08-12
    • Available Online: 2024-08-15
  • Publish Date: 2024-08-28
    Abstract
  • Achieving robust joint utilization of multidomain characteristics and deep-network features while maintaining a high jamming-recognition accuracy with limited samples is challenging. To address this issue, this paper proposes a multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming. This method involves first thoroughly extracting the multidomain characteristics of active jamming and then designing an optimization unit to automatically select effective characteristics and generate a text modality imbued with implicit expert knowledge. The text modality and involved time-frequency transformation image are separately fed into text and image encoders to construct multimodal-feature pairs and map them to a high-dimensional space for modal alignment. The text features are used as anchors and a guide to time-frequency image features for aggregation around the anchors through contrastive learning, optimizing the image encoder’s representation capability, achieving tight intraclass and separated interclass distributions of active jamming. Experiments show that compared to existing methods, which involve directly combining multidomain characteristics and deep-network features, the proposed guided-joint method can achieve differential feature processing, thereby enhancing the discriminative and generalization capabilities of recognition features. Moreover, under extremely small-sample conditions (2～3 training samples for each type of jamming), the accuracy of our method is 9.84% higher than those of comparative methods, proving the effectiveness and robustness of the proposed method.

     

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