采用残差神经网络的无人机遥控信号识别监测算法

李彬; 徐怡杭; 罗杰

doi:10.7652/xjtuxb202112017

您当前的位置：

首页 >

文章列表页 >

采用残差神经网络的无人机遥控信号识别监测算法

更新时间：2025-07-09

- 采用残差神经网络的无人机遥控信号识别监测算法
- A Recognition and Monitoring Algorithm for Drone Remote Control Signals Using Residual Neural Network
- 西安交通大学学报 2021年55卷第12期页码：146-154
- 作者机构：
  
  西北工业大学电子信息学院,西安,710072
- 作者简介：
- 基金信息：
  
  : 2021-04-21。作者简介: 李彬(1983—),男,博士,副教授。基金项目: 国家重点研发计划资助项目(2020YFB 1807004,2020YFB1807003)
- DOI：10.7652/xjtuxb202112017
  中图分类号： TM92
- 网络首发：2021-12-10，
  
  纸质出版：2021
- 稿件说明：
移动端阅览
李彬, 徐怡杭, 罗杰. 采用残差神经网络的无人机遥控信号识别监测算法[J]. 西安交通大学学报, 2021,55(12):146-154.

A Recognition and Monitoring Algorithm for Drone Remote Control Signals Using Residual Neural Network[J]. 2021, 55(12): 146-154.
李彬, 徐怡杭, 罗杰. 采用残差神经网络的无人机遥控信号识别监测算法[J]. 西安交通大学学报, 2021,55(12):146-154. DOI： 10.7652/xjtuxb202112017.

A Recognition and Monitoring Algorithm for Drone Remote Control Signals Using Residual Neural Network[J]. 2021, 55(12): 146-154. DOI： 10.7652/xjtuxb202112017.

摘要

为了解决无人机遥控信号在识别监测时易受随机噪声、窄带与宽带干扰影响

以及遥控信号跳频序列的跳频周期、跳频速率等特征难以准确提取的问题

提出了一种采用残差神经网络对无人机遥控信号时频谱图进行识别监测的算法(DRN-UAV)。首先通过滑动时间窗来读入时频谱图

并以联合自适应的方法计算信号频谱检测阈值; 然后对已读入的时频谱图进行二值化、剔除干扰等预处理操作

构造待测谱图; 接着将大量实测的不同机型遥控信号待测谱图作为数据集来训练和测试残差神经网络

进而避免跳频特征难以提取的问题; 最终由训练好的网络来实时识别当前遥控信号是否存在及其所属机型。DRN-UAV算法能克服遮挡及无人机体积等不利影响

是对基于雷达或光学的反无人机系统的有效补充。实验结果表明:DRN-UAV算法使单次识别耗时约缩短为传统读入方式的1/25; 在相同误检率下

DRN-UAV算法得到的信号频谱检测阈值相比传统方法降低了1.4 dBm

在不同硬件平台上都能有效增加检测范围; 当信噪比高于5.5 dB时

在单个窄带定频信号和WiFi干扰下

检测错误率能达到0.01%以内。

Abstract

An algorithm to monitor the control signal of drone using residual neural network is proposed to solve the problems that remote control signals of unmanned aerial vehicles(UAV)are usually susceptible to random noise and narrowband or broadband interference

and it is difficult to extract the frequency hopping period and rate of frequency hopping sequence of a remote control signal. Firstly

a time-spectrogram is obtained via a sliding time window

and a threshold of signal spectrum detection is calculated by a joint adaptive method. Then

pre-processing operations such as binarization and interference elimination are performed on the time-spectrogram to construct the time spectrum to be measured. Turther

a large number of processed spectrograms of different control signals are used as a data set to train and test the deep residual neural network

so as to avoid the problem of difficult extraction of frequency hopping features. Finally

the trained network is used to recognize the current remote control signal and its model in real time. The proposed DRN-UVA algorithm overcomes the adverse effects such as occlusion and UVA size

and is an effective supplement to anti-UVA system based on radar or optics. Experimental results show that the DRN-UAV algorithm shortens the single recognition time to about 1/25 of the traditional reading method. At the same error detection rate

the signal spectrum detection threshold obtained by the DRN-UAV algorithm reduces by 1.4 dBm compared with that of the traditional method and the detection range is effectively increased on different hardware platforms. When the signal-to-noise ratio is higher than 5.5 dB

the detection error rate can reach less that 0.01% under the interferences of single narrowband fixed-frequency signal and WiFi.

关键词

Keywords

references

张召忠. 无人机携炸弹袭击委总统, 谁干的? [N]. 科技日报, 2018-08-08(5).

杨慧君. 沙特油田遇袭: 低慢小目标防御是难点 [N]. 中国航天报, 2019-10-12(1).

张静, 张科, 王靖宇, 等. 低空反无人机技术现状与发展趋势 [J]. 航空工程进展, 2018, 9(1): 1-8,34.

ZHANG Jing, ZHANG Ke, WANG Jingyu, et al. Status and development trend of low-altitude anti-UAV technology [J]. Advances in Aeronautical Science and Engineering, 2018, 9(1): 1-8,34.

SHIN H, CHOI K, PARK Y, et al. Security analysis of FHSS-type drone controller [C]∥16th International Workshop on Information Security Applications. Berlin, Germany: Springer Verlag, 2016: 240-253.

HOLUBNYCHYI A G, KONAKHOVYCH G F. Spread-spectrum control channels for UAV based on the generalized binary Barker sequences [C]∥2013 IEEE 2nd International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings. Piscataway, NJ, USA: IEEE, 2013: 99-103.

TOPIC D, MALARIC K, MUMA D. Behavior of 2.4 GHz unmanned aerial vehicle(UAV)under interference [C]∥2018 First International Colloquium on Smart Grid Metrology. Piscataway, NJ, USA: IEEE, 2018: 1-4.

TIM F. Domestic drone countermeasures [EB/OL]. [2021-04-17]. http:∥www.ddcountermeasures.com /products.html.

PEACOCK M, JOHNSTONE M N. Towards detection and control of civilian unmanned aerial vehicles[EB/OL]. [2021-03-04]. https:∥ro.ecu.edu.au/cgi/viewcontent.cgi?referer=https:∥sc.panda321. com/httpsredir=1article=1051context=isw.

KIL H B, LEE J S, JEONG E R. Analysis of frequency hopping signals in commercial drones [J]. International Journal of Pure and Applied Mathematics, 2018, 118(19): 2015-2024.

刘丽. 民用无人机跳频信号分析与识别技术研究 [D]. 北京: 北京邮电大学, 2019: 29-33.

陈增茂, 程功, 孙志国, 等. 基于短时傅里叶变换的跳频信号频谱感知性能分析 [J]. 哈尔滨工程大学学报, 2021, 42(6): 902-907.

CHEN Zengmao, CHENG Gong, SUN Zhiguo, et al. Performance analysis of spectrum sensing of frequency-hopping signals under fading channels [J]. Journal of Harbin Engineering University, 2021, 42(6): 902-907.

ALIPOUR F A, DABAGHCHIAN A, WANG N, et al. Machine learning-based delay-aware UAV detection and operation mode identification over encrypted Wi-Fi traffic [J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 2346-2360.

MORITO T, SUGIYAMA O, KOJIMA R, et al. Partially shared deep neural network in sound source separation and identification using a UAV-embedded microphone array [C]∥2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2016: 1299-1304.

OLYAEI M H, JALALI H, NOORI A, et al. Fault detection and identification on UAV system with CITFA algorithm based on deep learning [C]∥26th Iranian Conference on Electrical Engineering. Piscataway, NJ, USA: IEEE, 2018: 988-993.

ZHAO J C, FU X R, YANG Z K, et al. UAV detection and identification in the internet of things [C]∥2019 15th International Wireless Communications Mobile Computing Conference. Piscataway, NJ, USA: IEEE, 2019: 1499-1503.

ZHANG H, CAO C, XU L, et al. A UAV detection algorithm based on an artificial neural network [J]. IEEE Access, 2018, 6: 24720-24728.

颜雨, 闫晓峰, 吴治霖. 一种复杂环境中跳频信号检测和参数估计改进方法 [J]. 通信技术, 2020, 53(3): 572-577.

YAN Yu, YAN Xiaofeng, WU Zhilin. Modified method for FH signal detection and parameter estimation in complex environment [J]. Communications Technology, 2020, 53(3): 572-577.

ATAPATTU S, TELLAMBURA C, JIANG Hai. Spectrum sensing via energy detector in low SNR [C]∥Proceedings of the 2011 IEEE International Conference on Communications. Piscataway, NJ, USA: IEEE, 2011: 1-5.

张萌, 王文, 任俊星, 等. 基于HOG-SVM的跳频信号检测识别算法 [J]. 信息安全学报, 2020, 5(3): 62-77.

ZHANG Meng, WANG Wen, REN Junxing, et al. Detection and recognition algorithm for frequency hopping signals based on HOG-SVM [J]. Journal of Cyber Security, 2020, 5(3): 62-77.

郑洋, 陈西豪, 朱锐, 等. 基于小波分解与希尔伯特-黄变换的跳频信号检测 [J]. 传感器与微系统, 2017, 36(9): 132-135.

ZHENG Yang, CHEN Xihao, ZHU Rui, et al. Frequency hopping signals detection based on wavelet decomposition and HHT [J]. Transducer and Microsystem Technologies, 2017, 36(9): 132-135.

EZUMA M, ERDEN, F, KUMAR A C, et al. UAV communications for 5G and beyond [M]. Hoboken, USA: Wiley Online Library, 2020: 71-102.

NEMER I, SHELTAMI T, AHMAD I, et al. RF-based UAV detection and identification using hierarchical learning approach [J]. Sensors, 2021, 21(6): 1947.

杨亚南. 无人机信号的侦收与识别方法研究 [D]. 成都: 电子科技大学, 2018: 54-72.

陈君胜, 杨小勇, 徐怡杭. 基于遥控信号频谱特征的无人机识别算法 [J]. 无线电工程, 2019, 49(2): 101-106.

CHEN Junshen, YANG Xiaoyong, XU Yihang. UAV recognition algorithm based on spectrum characteristics of remote control signal [J]. Radio Engineering, 2019, 49(2): 101-106.

KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.

HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]∥IEEE Conference on Computer Vision Pattern Recognition. Piscataway, NJ, USA: IEEE Computer Society, 2016: 770-778.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

无人艇视觉系统多类水面目标特征提取与识别

使用改进残差神经网络的滚动轴承变工况故障诊断方法

采用混合域注意力机制的无人机识别方法

张角凹坑气膜孔对双层壁气膜冷却特性的影响

结构-语义-残差协同的图像压缩框架：面向隐私保护与高保真重建