基于归一化循环前缀相关谱的无人机识别技术

张涵硕; 李涛; 李勇朝; 温志津

doi:10.19665/j.issn1001-2400.20230704

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基于归一化循环前缀相关谱的无人机识别技术

信息与通信工程 | 更新时间：2024-06-03

- 基于归一化循环前缀相关谱的无人机识别技术
- Drone identification based on the normalized cyclic prefix correlation spectrum
- 西安电子科技大学学报 2024年51卷第2期页码：68-75
- 作者机构：
  
  1. 西安电子科技大学通信工程学院,陕西西安 710071
  2. 电磁空间认知与智能控制技术实验室,北京 100191
- 作者简介：
  
  [ "张涵硕(1994—),男,西安电子科技大学博士研究生,E-mail:[email protected]; " ]
  李涛(1989—),男,讲师,E-mail:[email protected]
  [ "李勇朝(1974—),男,教授,E-mail:[email protected]; " ]
  [ "温志津(1971—),男,正高级工程师,E-mail:[email protected]" ]
- 基金信息：
  
  陕西省重点研发计划(2023ZDLGY-33);陕西省重点研发计划(2022ZDLGY05-03);陕西省重点研发计划(2022ZDLGY05-04);中央高校基本科研专项资金(XJS220116);国家自然科学基金(62001358)
- DOI：10.19665/j.issn1001-2400.20230704
  中图分类号： TN911.7
- 纸质出版日期：2024-4-20，
  
  网络出版日期：2023-9-14，
  
  收稿日期：2023-3-3，
扫描看全文
张涵硕, 李涛, 李勇朝, 等. 基于归一化循环前缀相关谱的无人机识别技术[J]. 西安电子科技大学学报, 2024,51(2):68-75.

Hanshuo ZHANG, Tao LI, Yongzhao LI, et al. Drone identification based on the normalized cyclic prefix correlation spectrum[J]. Journal of Xidian University, 2024,51(2):68-75.
张涵硕, 李涛, 李勇朝, 等. 基于归一化循环前缀相关谱的无人机识别技术[J]. 西安电子科技大学学报, 2024,51(2):68-75. DOI： 10.19665/j.issn1001-2400.20230704.

Hanshuo ZHANG, Tao LI, Yongzhao LI, et al. Drone identification based on the normalized cyclic prefix correlation spectrum[J]. Journal of Xidian University, 2024,51(2):68-75. DOI： 10.19665/j.issn1001-2400.20230704.

摘要

基于射频的无人机识别技术具有探测距离长、环境依赖性低的优点

已成为无人机监控系统不可或缺的技术手段。如何在低信噪比条件下有效识别无人机是当前热点问题。为保证良好的图传质量

无人机通常采用带有循环前缀结构的正交频分复用(OFDM)调制作为图传链路的调制方式。据此特性

首先提出一种基于归一化循环前缀相关谱和卷积神经网络的无人机识别算法。依据对无人机信号的OFDM符号周期和循环前缀长度的分析结果

计算信号归一化循环前缀相关谱。当归一化循环前缀相关谱的计算参数与无人机信号的调制参数匹配时

谱线中会出现若干相关峰

峰的位置分布反映了无人机信号帧结构、突发规则等协议特征。然后

利用卷积神经网络对归一化循环前缀相关谱进行特征分析和提取

从而识别无人机。最后

利用通用软件无线电平台USRP X310对5款无人机的射频信号进行采集

构建实验数据集。实验结果表明

该算法优于基于频谱和基于时频谱的算法

且在低信噪比下仍然有效。

Abstract

Radio-frequency(RF)-based drone identification technology has the advantages of long detection distance and low environmental dependence

so that it has become an indispensable approach to monitoring drones.How to identify a drone effectively at the low signal-to-noise ratio(SNR) regime is a hot topic in current research.To ensure excellent video transmission quality

drones commonly adopt orthogonal frequency division multiplexing(OFDM) modulation with cyclic prefix(CP) as the modulation of video transmission links.Based on this property

we propose a drone identification algorithm based on the convolutional neural network(CNN) and normalized CP correlation spectrum.Specifically

we first analyze the OFDM symbol durations and CP durations of drone signals

on the basis of which the normalized CP correlation spectrum is calculated.When the modulation parameters of a drone signal match the calculated normalized CP correlation spectrum

several correlation peaks will appear in the normalized CP correlation spectrum.The positions of these peaks reflect the protocol characteristics of drone signals

such as frame structure and burst rules.Finally

for identifying drones

a CNN is trained to extract these characteristics from the normalized CP correlation spectrum.In this work

a universal software radio peripheral(USRP) X310 is utilized to collect the RF signals of five drones to construct the experimental dataset.Experimental results show that the proposed algorithm performs better than spectrum-based and spectrogram-based algorithms

and it remains effective at low SNRs.

关键词

无人机射频信号OFDM归一化循环前缀相关谱

Keywords

dronesRF signalOFDMCP correlation spectrum

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