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Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model
Shuolei Li1, Bin Liu1, Lin Zhou1
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|May 14, 2025
Summary
A new Residual Attention Network (RA-Net) effectively recognizes chaff clouds, a common radar jamming method. This advanced model achieves 97.10% accuracy, outperforming traditional techniques and demonstrating strong generalization capabilities.
Area of Science:
- Radar systems
- Electronic warfare
- Signal processing
Background:
- Chaff clouds are a prevalent passive jamming technique posing significant challenges for radar systems.
- Effective countermeasures against chaff cloud interference are crucial for maintaining radar performance.
Purpose of the Study:
- To develop an advanced method for recognizing chaff cloud High-Resolution Range Profiles (HRRP).
- To enhance the anti-chaff jamming capabilities of radar systems.
Main Methods:
- Proposed a novel Residual Attention Network (RA-Net) incorporating an attention mechanism.
- Focused on extracting informative and stable hierarchical features from HRRP data.
- Established a comprehensive dataset of chaff cloud HRRP data through extensive field experiments.
Main Results:
- RA-Net achieved a superior recognition accuracy of 97.10% on measured HRRP data.
- The proposed network demonstrated excellent generalization capability.
- RA-Net outperformed traditional chaff cloud recognition methods.
Conclusions:
- RA-Net offers a significant advancement in chaff cloud HRRP recognition.
- The developed dataset provides a critical resource for future research.
- RA-Net sets a new benchmark for chaff cloud recognition accuracy and performance.
