基于图像分割和融合注意力残余网络的雷达复合干扰识别
Peishan Li1, Jian Yang1, Jiaao Lin1
1China Academy of Launch Vehicle Technology, No. 165, South 4th Ring East Road, Fengtai District, Beijing 100076, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种用于细分和识别复杂雷达干扰信号的新方法. 该方法实现了高精度,即使对于以前未见的复合干扰类型.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 现代电磁环境给雷达系统带来了复杂的挑战,包括复杂的复合干扰.
- 复合干扰是多种信号类型的附加组合,由于其多样化的模式,很难识别.
研究的目的:
- 开发一种有效的细分和识别复合干扰信号的方法.
- 提高干扰识别系统的稳定性和一般化能力.
主要方法:
- 利用时间频域表示的图像细分技术 (通过短时间里埃转换 (STFT) 获得) 来进行干扰细分.
- 开发了一种增强的剩余网络 (ResNet),具有空间通道融合注意力机制 (SCFAM),用于特征提取和信号识别.
主要成果:
- 对于复合干扰信号,实现了98.60%的高识别精度.
- 与复合干扰识别中的三种经典方法相比,证明了卓越的性能.
- 在识别未经训练的复合干扰类型方面表现出色,表明强大的稳定性和通用性.
结论:
- 拟议的方法有效地在复杂的电磁环境中对复合干扰信号进行细分和识别.
- 在ResNet中整合注意力机制提高了捕获多层次特征的能力,以改善干扰识别.
- 该方法为先进的雷达电子战应用提供了强大的和可通用的解决方案.
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