低信噪比条件下无人机射频信号实时检测方法  

A Real Time Drone RF Signal Detection Method Under Low SNR Condition

作　　者：苏志刚[1] 晏翔 韩冰 郝敬堂[1] 赵欣怡 SU Zhigang;YAN Xiang;HAN Bing;HAO Jingtang;ZHAO Xinyi(Sino-European Institute of Aviation Engineering,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学中欧航空工程师学院,天津300300

出　　处：《信号处理》2023年第5期919-928,共10页Journal of Signal Processing

基　　金：国家自然科学基金青年科学基金(62103417);中央高校基本科研业务费中国民航大学专项(3122019183);中国民航大学研究生科研创新项目(2021YJS071)。

摘　　要：无人机射频信号检测技术是实现未来空管系统中自动化无人机检测的关键技术。针对低信噪比条件下无人机射频信号检测准确率较低的问题,本文提出了一种基于多分支卷积结构以及注意力机制的射频信号检测模型。该方法首先设计了一种由多分支卷积结构组成的轻量化骨干网络来提取信号完整时频谱中的微小时变特征,提升模型对抗噪声的能力。其次使用注意力机制增强模型对时频谱中的信号区域的表达,抑制对噪声区域的关注,进一步提升信号检测准确率。本文在两个公开数据集上开展实验评估模型性能,对比实验结果表明本文提出的检测模型可以有效地在低信噪比条件下提升无人机射频信号检测准确率,在-15 dB至-6 dB的低信噪比范围下的检测准确率可分别达到94.63%和94.75%,-15 dB至15 dB全信噪比范围下的准确率分别为97.50%和97.35%,较以往方法有大幅提升。消融实验结果表明,多分支结构和注意力机制可分别带来3.74%和1.56%的性能提升。推理速度测试实验表明本文模型的推理时间仅需1.61 ms,可以运用于无人机信号的实际检测系统中。The drone RF signal identification technology is the key technology to realize automatic drone identification in nextgeneration air traffic control systems.Aiming to improve the drone radio frequency signals identification accuracy under low signal-to-noise ratios,this paper proposed a radio frequency signal identification model based on the multi-branch convolution structure and attention mechanism.Firstly,a lightweight backbone network composed of the multi-branch convolution structure was designed to extract the small time-varying features in the signal time-frequency spectrum.Secondly,the attention mechanism was used to enhance the expression of the signal region in the time-frequency spectrum,restrain the attention to the noise region,and further improve the signal identification accuracy.This paper carried out experiments on two open datasets to evaluate the performance of the model.The comparative experimental results show that the proposed model can effectively improve the identification accuracy of drone radio frequency signals under low signal-to-noise ratio conditions.The identification accuracy can reach 94.63%and 94.75%respectively in the low SNR range of-15 dB to-6 dB,and 97.50%and 97.35%respectively in the full signal-to-noise ratios range of-15 dB to 15 dB,which is significantly improved compared with the previous methods.The results of ablation experiments show that the multi-branch structure and attention mechanism can improve the performance by 3.74%and 1.56%respectively.The inference speed test experiment shows that the inference time of this model is only 1.61 ms,which can be used in the actual drone signals identification systems.

关 键 词：无人机射频信号检测 多分支卷积网络 注意力机制 时频分析 

分 类 号：TN98[电子电信—信息与通信工程]