对雷达复合干扰多特征融合和轻量识别的研究
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
概括
这项研究引入了一种轻量级网络,用于识别雷达复合干扰,即使在低信号条件下,也能达到87%以上的准确性. 这种高效模型非常适合电子反对策应用.
科学领域:
- 雷达系统工程 雷达系统工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 复杂的电磁环境给雷达系统带来了挑战,原因是复杂的干扰技术.
- 准确识别复合干扰对于有效的电子反制措施 (ECCM) 操作至关重要.
研究的目的:
- 开发一种轻量级,高精度的网络,用于在具有挑战性的条件下识别雷达复合干扰.
- 为了平衡识别性能与实际ECCM部署的计算复杂性.
主要方法:
- 使用了三个互补的时间频率表示,用于复合干扰的多特征提取.
- 采用了多分支架构,具有注意力机制,用于并行,多层次的特征学习和增强.
- 集成功能采用加权聚变策略,并结合了轻量级的GSENet模块.
主要成果:
- 在低干扰噪声比 (JNR) 条件下,为七种复合干扰类型实现了超过87%的识别精度.
- 拟议的网络具有较低的参数数,低于0.14万.
- 证明了识别性能和模型复杂性之间的有效权衡.
结论:
- 开发的轻量级多功能融合网络对于雷达复合物干扰识别是有效的.
- 该网络的效率和准确性使其适合实时ECCM应用.
- 该方法为提高复杂电磁环境中的雷达弹性提供了可行的解决方案.
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