在频谱传感中用于宽带信号检测和时间频率定位的起止点CenterNet
Teng Cheng1, Lei Sun1, Junning Zhang2
1School of Automotive and Transportation Engineering, Hefei University of Technology, China; Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province, Hefei, 230000, China.
概括
本研究介绍了用于检测宽带信号的快速频谱大小自训网络 (FSSNet),克服了现有的深度学习方法在识别不连续爆发信号方面的局限性. 通过使用时间频域中的开始-停止点特征,FSSNet可以有效地检测复杂的信号.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 认知无线电是一种认知无线电.
背景情况:
- 深度学习 (DL) 方法对于广带多信号检测在认知无线电频谱传感中至关重要.
- 现有的DL方法在不连续的爆发信号,广泛的动态带宽和多个频道方面扎.
研究的目的:
- 提出一个新的方案,即快速频谱大小自训网络 (FSSNet),用于准确的宽带多信号检测.
- 解决当前DL方法在检测不连续和复杂信号方面的局限性.
主要方法:
- 拟议的FSSNet使用启动-停止点信号特征来检测宽带信号.
- 引入了使用LPS-YXE的快速开始-停止热图,以标记和划分2D热图中的信号开始-停止点.
- 热图的多维空间转换有助于定位起止点和特征提取.
主要成果:
- 通过多维热图转换,FSSNet在检测不连续信号方面表现出有效性.
- 网络通过对2D Box估计的单个变量进行回归来实现令人满意的检测速度.
- 模拟结果通过实用信号验证了拟议的基于启动停止的检测方案的优越性.
结论:
- FSSNet方案为宽带多信号检测提供了一个强大的解决方案,特别是对于不连续的信号.
- 创新地使用起止点和热图转换,提高了检测的准确性和速度.
- 这项研究为可用的代码和模型的认知无线电频谱传感提供了宝贵的贡献.
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