基于多模式特征融合的时间频率别名信号识别
Hailong Zhang1, Lichun Li1, Hongyi Pan1
1School of Information Engineering, University of Information Engineering, Zhengzhou 450000, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种新的多模式融合方法 (TRMM),用于识别宽带接收中的时间频率异化信号. TRMM方法的准确度超过97.3%,克服了传统分离技术的局限性.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 通信工程 通信工程
背景情况:
- 在宽带信号接收中,通过时频别化识别多源信号是具有挑战性的.
- 由于存在重大错误,传统的分离-首先方法在高别名条件下失败.
- 单模识别方法缺乏足够的信号信息来准确识别.
研究的目的:
- 提出一种强大的方法来识别时间频率别名信号.
- 克服现有的信号分离和单模式识别技术的局限性.
- 为了提高识别宽带接收中复杂的别名信号的准确性.
主要方法:
- 提出了一种基于多模式融合 (TRMM) 的新型时频别化信号识别方法.
- 使用U-Net网络从时间频率和波频率图像中提取像素级特征.
- 多式联运特征的加权融合用于分类.
主要成果:
- 该TRMM方法有效地识别时间频率别名信号.
- 在0dB SNR的四信号别名模型中,实现了超过97.3%的识别率.
- 与传统方法相比,拟议的方法显示出更高的性能.
结论:
- TRMM方法为时间频率别名信号识别提供了有效的解决方案.
- 多模式融合提高了信号识别的准确性和稳定性.
- 这种方法在宽带信号接收和分析领域取得了进展.
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