National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2022 May 21;22(10):3909. doi: 10.3390/s22103909.
Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network's input to localize the spectral locations of the signals. In the proposed framework, Welch's periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.
多信号检测在民用和军事领域都具有重要意义，如认知无线电 (CR)、频谱监测和信号侦察等，它是指在观测频段中联合检测多个信号的存在，并估计它们的载波频率和带宽。在这项工作中，提出了一种基于深度学习的框架 SigdetNet，它采用功率谱作为网络的输入，以定位信号的频谱位置。在提出的框架中，应用 Welch 周期图来减小功率谱密度 (PSD) 的方差，然后进行对数变换以增强信号。特别地，构建了一个具有嵌入金字塔池化模块的编码器-解码器网络，旨在提取与信号检测相关的多尺度特征。研究了频率分辨率、网络架构和损失函数对检测性能的影响。进行了广泛的仿真实验，结果表明所提出的多信号检测方法比其他基准方案具有更好的性能。
