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DFN-YOLO:在宽带频谱中检测窄带信号
Kun Jiang1,2, Kexiao Peng1,3, Yuan Feng1,3
1National Key Laboratory of Intelligent Spatial Information, Beijing 100029, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了DFN-YOLO,这是一种用于检测宽带环境中的窄带信号的新型模型,即使信号噪声比 (SNR) 低. DFN-YOLO显著提高了无线频谱传感应用的检测精度和时间估计.
科学领域:
- 无线通信是一种无线通信.
- 信号处理 信号处理
- 机器学习用于信号检测.
背景情况:
- 高效的频谱利用对于现代无线通信至关重要.
- 在宽带环境中检测窄带信号,特别是低信号噪声比 (SNR) 时,由于时间频率特征和噪声的复杂性,具有挑战性.
- 现有的物体检测模型与宽带频谱传感的细微差别作斗争.
研究的目的:
- 开发一个强大的信号检测模型,用于在宽带场景中盲目检测信号.
- 增强道特征的提取和集成,以改善信号识别.
- 在复杂的无线环境中在低SNR条件下实现更高的检测精度.
主要方法:
- 介绍可变形功能增强网络-你只看一次 (DFN-YOLO) 模型.
- 将可变形通道特征融合网络 (DCFFN) 与可变形注意力机制集成.
- 使用Focal Scaled Intersection over Union (Focal_SIoU) 对损失函数进行优化.
- 构建和使用专门的信号检测数据集进行评估.
主要成果:
- 在宽带时频谱图上,DFN-YOLO实现了0.850的平均平均精度 (mAP50-95).
- 该模型的性能明显优于主流的物体检测模型,包括YOLOv8.
- 保持了在5.55×10-5秒内平均时间估计误差,并提供了初步的中心频率估计.
结论:
- 在宽带环境中,DFN-YOLO在盲点信号检测方面表现出卓越的性能.
- 该模型处理低SNR条件和复杂特征的能力提供了显著的优势.
- 这些发现对民用和军事无线通信应用都有重大影响.
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