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Research on Multi-Feature Fusion and Lightweight Recognition for Radar Compound Jamming
Weiyu Zha1, Jianyin Cao1, Hao Wang1,2
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|February 27, 2026
Summary
This study introduces a lightweight network for recognizing radar compound jamming, achieving over 87% accuracy even in low signal conditions. The efficient model is ideal for electronic counter-countermeasure applications.
Area of Science:
- Radar Systems Engineering
- Artificial Intelligence
- Signal Processing
Background:
- Complex electromagnetic environments pose challenges for radar systems due to sophisticated jamming techniques.
- Accurate recognition of compound jamming is crucial for effective electronic counter-countermeasure (ECCM) operations.
Purpose of the Study:
- To develop a lightweight, high-accuracy network for recognizing radar compound jamming under challenging conditions.
- To balance recognition performance with computational complexity for practical ECCM deployment.
Main Methods:
- Utilized three complementary time-frequency representations for multi-feature extraction of compound jamming.
- Employed a multi-branch architecture with attention mechanisms for parallel, multi-scale feature learning and enhancement.
- Integrated features using a weighted fusion strategy and incorporated the lightweight GSENet module.
Main Results:
- Achieved over 87% recognition accuracy for seven compound jamming types under low jamming-to-noise ratio (JNR) conditions.
- The proposed network has a low parameter count, below 0.14 million.
- Demonstrated an effective trade-off between recognition performance and model complexity.
Conclusions:
- The developed lightweight multi-feature fusion network is effective for radar compound jamming recognition.
- The network's efficiency and accuracy make it suitable for real-time ECCM applications.
- The approach offers a viable solution for enhancing radar resilience in complex electromagnetic environments.
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