Siamese Network-assisted Multi-domain Feature Fusion for Radar Active Jamming Recognition Method

LI Ning; WANG Zan; SHU Gaofeng; ZHANG Tingwei; GUO Zhengwei

doi:10.11999/JEIT240797

Volume 47 Issue 6

Jun. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(6): 1837-1849

LI Ning, WANG Zan, SHU Gaofeng, ZHANG Tingwei, GUO Zhengwei. Siamese Network-assisted Multi-domain Feature Fusion for Radar Active Jamming Recognition Method[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1837-1849. doi: 10.11999/JEIT240797

Citation:

LI Ning, WANG Zan, SHU Gaofeng, ZHANG Tingwei, GUO Zhengwei. Siamese Network-assisted Multi-domain Feature Fusion for Radar Active Jamming Recognition Method[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1837-1849. doi: 10.11999/JEIT240797

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Siamese Network-assisted Multi-domain Feature Fusion for Radar Active Jamming Recognition Method

doi: 10.11999/JEIT240797 cstr: 32379.14.JEIT240797

1.
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2.
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China
3.
Henan Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng 475004, China

Funds: The Natural Science Foundation of Henan (242300421170)

Received Date: 2024-09-14
Rev Recd Date: 2025-04-18

Available Online: 2025-05-08

Publish Date: 2025-06-30

Abstract

Abstract

Objective The rapid development of electronic warfare technology has introduced complex scenarios in which active jamming presents considerable challenges to radar systems. On modern battlefields, the electromagnetic environment is highly congested, and various forms of active jamming signals frequently disrupt radar functionality. Although existing recognition algorithms can identify certain types of radar active jamming, their performance declines under low Jamming-to-Noise Ratio (JNR) conditions or when training data are scarce. Low JNR reduces the detectability of jamming signals by conventional methods, and limited sample size further constrains recognition accuracy. To address these challenges, neural network-based methods have emerged as viable alternatives. This study proposes a radar active jamming recognition approach based on multi-domain feature fusion assisted by a Siamese network, which enhances recognition capability under low JNR and small-sample conditions. The proposed method offers an intelligent framework for improving jamming recognition in complex environments and provides theoretical support for battlefield awareness and the design of effective counter-jamming strategies. Methods The proposed method comprises a multi-domain feature fusion subnetwork, a Siamese architecture, and a joint loss design. To extract jamming features effectively under low JNR conditions, a multi-domain feature fusion subnetwork is developed. Specifically, a semi-soft thresholding shrinkage module is proposed by integrating a semi-soft threshold function with an attention mechanism. This module efficiently extracts time-domain features and eliminates the limitations of manual threshold selection. To enhance the extraction of time-frequency domain features, a multi-scale convolution module and an additional attention mechanism are incorporated. To reduce the model’s dependence on large training datasets, a weight-sharing Siamese network is constructed. By comparing similarity between sample pairs, this network increases the number of training iterations, thereby mitigating the limitations imposed by small sample sizes. Finally, three loss functions are jointly applied: an improved weighted contrastive loss, an adaptive cross-entropy loss, and a triplet loss. This joint strategy promotes intra-class compactness and inter-class separability of jamming features. Results and Discussions When the number of training samples is limited (Table 6), the proposed method achieves an accuracy of 96.88% at a JNR of –6 dB with only 20 training samples, indicating its effectiveness under data-scarce conditions. With further reduction in sample size—specifically, when only 15 training samples are available per jamming type—the recognition performance of other methods declines substantially. In contrast, the proposed method maintains higher recognition accuracy, demonstrating enhanced stability and robustness under low JNR and limited sample conditions. This performance advantage is attributable to three key factors: (1) Multi-domain feature fusion integrates jamming features from multiple domains, preventing the loss of discriminative information commonly observed under low JNR conditions. (2) The weight-sharing Siamese network increases the number of effective training iterations by evaluating sample similarities, thereby mitigating the limitations associated with small datasets. (3) The combined use of an improved weighted contrastive loss, an adaptive cross-entropy loss, and a triplet loss promotes intra-class compactness and inter-class separability of jamming features, enhancing the model’s generalization capability. Conclusions This study proposes a radar active jamming recognition method that performs effectively under low JNR and limited training sample conditions. A multi-domain feature fusion subnetwork is developed to extract representative features from both the time and time-frequency domains, enabling a more comprehensive and discriminative characterization of jamming signals. A weight-sharing Siamese network is then introduced to reduce reliance on large training datasets by leveraging sample similarity comparisons to expand training iterations. In addition, three loss functions—an improved weighted contrastive loss, an adaptive cross-entropy loss, and a triplet loss—are jointly applied to promote intra-class compactness and inter-class separability. Experimental results validate the effectiveness of the proposed method. At a low JNR of –6 dB with only 20 training samples, the method achieves a recognition accuracy of 96.88%, demonstrating its robustness and adaptability in challenging electromagnetic environments. These findings provide technical support for the development of anti-jamming strategies and enhance the operational reliability of radar systems in complex battlefield scenarios.
- Radar active jamming recognition,
- Siamese network,
- Multi-domain features,
- Attention mechanism

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References(16)

References

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