In this paper, we optimize a faster region-based convolutional neural network (FRCNN) for 1-dimensional (1D) signal processing and electromagnetic spectrum sensing. We target a cluttered radio frequency (RF) environment, where multiple RF transmission can be present at various frequencies with different bandwidths. The challenge is to accurately and quickly detect and localize each signal with minimal prior information of the signal within a band of interest. As the number of wireless devices grow, and devices become more complex from advances such as software defined radio (SDR), this task becomes increasingly difficult. It is important for sensing devices to keep up with this change, to ensure optimal spectrum usage, to monitor traffic over-the-air for security concerns, and for identifying devices in electronic warfare. Machine learning object detection has shown to be effective for spectrum sensing, however current techniques can be slow and use excessive resources. FRCNN has been applied to perform spectrum sensing using 2D spectrograms, however is unable to be applied directly to 1D signals. We optimize FRCNN to handle 1D signals, including fast Fourier transform (FFT) for spectrum sensing. Our results show that our method has better localization performance, and is faster than the 2D equivalent. Additionally, we show a use case where the modulation type of multiple uncooperative transmissions is identified. Finally, we prove our method generalizes to real world scenarios, by testing it over-the-air using SDR.

翻译：本文优化了基于区域的快速卷积神经网络（FRCNN）用于一维（1D）信号处理与电磁频谱感知。我们针对复杂的射频（RF）环境，其中多个射频传输可能出现在不同频率和带宽下。挑战在于，在目标频段内仅凭最少的先验信息，准确且快速地检测并定位每个信号。随着无线设备数量的增长以及软件定义无线电（SDR）等技术进步带来的设备复杂性增加，该任务变得日益困难。传感设备必须适应这种变化，以确保最优频谱利用、监控空中流量以保障安全、并在电子战中识别设备。机器学习目标检测已被证明对频谱感知有效，但现有技术可能速度较慢且资源消耗过大。FRCNN已被应用于利用二维（2D）频谱图进行频谱感知，但无法直接应用于一维信号。我们优化了FRCNN以处理一维信号，包括用于频谱感知的快速傅里叶变换（FFT）。结果表明，我们的方法具有更好的定位性能，且速度快于二维等效方法。此外，我们展示了在未合作多传输场景下识别调制类型的用例。最后，通过使用SDR进行空中测试，证明了我们的方法能够泛化到真实世界场景。