Jiang Kun, Peng Kexiao, Feng Yuan, Guo Xia, Tang Zuping
National Key Laboratory of Intelligent Spatial Information, Beijing 100029, China.
Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China.
Sensors (Basel). 2025 Jul 5;25(13):4206. doi: 10.3390/s25134206.
With the rapid development of wireless communication technologies and the increasing demand for efficient spectrum utilization, broadband spectrum sensing has become critical in both civilian and military fields. Detecting narrowband signals under broadband environments, especially under low-signal-to-noise-ratio (SNR) conditions, poses significant challenges due to the complexity of time-frequency features and noise interference. To this end, this study presents a signal detection model named deformable feature-enhanced network-You Only Look Once (DFN-YOLO), specifically designed for blind signal detection in broadband scenarios. The DFN-YOLO model incorporates a deformable channel feature fusion network (DCFFN), replacing the concatenate-to-fusion (C2f) module to enhance the extraction and integration of channel features. The deformable attention mechanism embedded in DCFFN adaptively focuses on critical signal regions, while the loss function is optimized to the focal scaled intersection over union (Focal_SIoU), improving detection accuracy under low-SNR conditions. To support this task, a signal detection dataset is constructed and utilized to evaluate the performance of DFN-YOLO. The experimental results for broadband time-frequency spectrograms demonstrate that DFN-YOLO achieves a mean average precision (mAP50-95) of 0.850, averaged over IoU thresholds ranging from 0.50 to 0.95 with a step of 0.05, significantly outperforming mainstream object detection models such as YOLOv8, which serves as the benchmark baseline in this study. Additionally, the model maintains an average time estimation error within 5.55×10-5 s and provides preliminary center frequency estimation in the broadband spectrum. These findings underscore the strong potential of DFN-YOLO for blind signal detection in broadband environments, with significant implications for both civilian and military applications.
随着无线通信技术的快速发展以及对高效频谱利用需求的不断增加，宽带频谱感知在民用和军事领域都变得至关重要。在宽带环境下检测窄带信号，尤其是在低信噪比（SNR）条件下，由于时频特征的复杂性和噪声干扰，带来了重大挑战。为此，本研究提出了一种名为可变形特征增强网络-你只看一次（DFN-YOLO）的信号检测模型，专门用于宽带场景下的盲信号检测。DFN-YOLO模型包含一个可变形通道特征融合网络（DCFFN），取代了拼接转融合（C2f）模块以增强通道特征的提取和整合。嵌入在DCFFN中的可变形注意力机制自适应地聚焦于关键信号区域，同时将损失函数优化为焦点缩放交并比（Focal_SIoU），提高了低信噪比条件下的检测精度。为支持此任务，构建并利用了一个信号检测数据集来评估DFN-YOLO的性能。宽带时频频谱图的实验结果表明，DFN-YOLO在交并比阈值从0.50到0.95、步长为0.05的范围内实现了0.850的平均精度均值（mAP50-95），显著优于作为本研究基准基线的主流目标检测模型，如YOLOv8。此外，该模型的平均时间估计误差保持在5.55×10-5 s以内，并在宽带频谱中提供初步的中心频率估计。这些发现凸显了DFN-YOLO在宽带环境下进行盲信号检测的强大潜力，对民用和军事应用都具有重要意义。
