电讯技术2025，Vol.65Issue(11)：1737-1746,10.DOI:10.20079/j.issn.1001-893x.240712001
利用多尺度卷积注意力的宽带信号稀疏检测方法
A Sparse Detection Method for Broadband Signals by Utilizing Multi-scale Convolutional Attention
摘要
Abstract
In broadband reconnaissance scenarios,achieving high signal detection accuracy often entails significant computational costs.To address this,a multi-scale convolution attention sparse detection(MSCA-S)method is proposed,which incorporates prior knowledge of signal spectrograms by capturing long-range temporal dependencies and suppressing irrelevant frequency-domain interference.MSCA-S introduces a multi-scale horizontal convolution attention(MSHCA)mechanism that jointly extracts multi-dimensional signal features,enhancing detection accuracy while reducing computational complexity through horizontal convolution.Building on MSHCA,a hierarchically stacked broadband signal detection framework is developed,and sparse feature parameters are used to further optimize computational efficiency.MSCA-S is evaluated on a real-world and simulated broadband signal dataset(2.5 MHz spectrum)collected in Qingdao,achieving an average detection accuracy of 95.6%across varying signal-to-noise ratios.Compared with the frequency-sensitive signal detector,the Swin-Transformer-based protocol recognition method,and the Res-101 detection method,MSCA-S improves accuracy by 0.05%,2.94%,and 6.14%,respectively,while reducing computational costs by 1.53×1010,1.79×1010,and 4.59×1010,respectively.关键词
宽带信号检测识别/注意力机制/多尺度卷积/稀疏算法Key words
broadband signal detection and recognition/attention mechanisms/multi-scale convolution/sparse algorithms分类
电子信息工程引用本文复制引用
龚安,张静蕾,郭兰图,赵晓蕾,刘玉超..利用多尺度卷积注意力的宽带信号稀疏检测方法[J].电讯技术,2025,65(11):1737-1746,10.基金项目
国家自然科学基金重点项目(U20B2038) （U20B2038）
