混合信号的细粒度识别与几何坐标注意力
Qingwu Yi1,2, Qing Wang2, Jianwu Zhang3
1National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了一种用于分类复杂混合信号的新型模型,提高了不同信号类型的识别精度. 几何坐标注意力机制改善了特征提取,导致更好的混合信号分类.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 现代技术增加了信号调制的复杂性,导致时间频率重叠.
- 这种重叠挑战了混合信号的准确分类和识别,降低了性能和通用性.
研究的目的:
- 开发混合信号的细粒度识别模型.
- 为了提高混合信号分类的准确性和通用性.
主要方法:
- 提出了一种使用残余神经网络骨干的新型识别模型.
- 引入了几何坐标注意力机制,用于时间频率加权分析.
- 整合了一个低级别的双线聚合模块,用于细粒度特征融合.
主要成果:
- 该模型在精确匹配比率方面实现了9%的改进.
- 与基线相比,Hamming分数有5%的改善.
- 在39600个时间频率图的模拟数据集上的实验结果证实了有效性.
结论:
- 拟议的模型显著提高了混合信号分类能力.
- 几何坐标注意力和低级双线聚合模块改善了特征提取和融合.
- 该模型显示复杂的混合信号的识别和概括性得到了改进.
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