A Frequency Hopping Network Station Sorting Algorithm Based on Improved YOLOv8

ZHU Zheng-yu; ZHAO Hang-ran; WANG Zi-xuan; WANG Zhong-yong; KONG Ke-xian; LIANG Jing

doi:10.12263/DZXB.20240487

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A Frequency Hopping Network Station Sorting Algorithm Based on Improved YOLOv8

PAPERS | 更新时间：2025-12-08

- A Frequency Hopping Network Station Sorting Algorithm Based on Improved YOLOv8
  增强出版
- ACTA ELECTRONICA SINICA Vol. 53, Issue 2, Pages: 385-394(2025)
- 作者机构：
  
  1.郑州电气与信息工程学院，河南郑州 450001
  2.东南大学移动通信国家重点实验室，江苏南京 210018
  3.河南省智能网络和数据分析国际联合实验室，河南郑州 450001
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFD2001200);National Natural Science Foundation of China(61922072);Program for Science & Technology Innovation Talents in Universities of Henan Province(23HASTIT019);Natural Science Foundation of Henan Province(232300421097);Open Research Fund of National Mobile Communications Research Laboratory Southeast University(2023D11);Program for Science & Technology Innovation Talents in Universities of Henan Province(ISN25-24)
- DOI：10.12263/DZXB.20240487
  CLC： TN91;
- Received：27 May 2024，
  
  Revised：2024-10-28，
  
  Published：25 February 2025
- 稿件说明：
移动端阅览
朱政宇, 赵航冉, 王梓晅, 等. 一种改进YOLOv8的跳频网台分选算法[J]. 电子学报, 2025, 53(02): 385-394.

ZHU Zheng-yu, ZHAO Hang-ran, WANG Zi-xuan, et al. A Frequency Hopping Network Station Sorting Algorithm Based on Improved YOLOv8[J]. Acta Electronica Sinica, 2025, 53(02): 385-394.
朱政宇, 赵航冉, 王梓晅, 等. 一种改进YOLOv8的跳频网台分选算法[J]. 电子学报, 2025, 53(02): 385-394. DOI：10.12263/DZXB.20240487

ZHU Zheng-yu, ZHAO Hang-ran, WANG Zi-xuan, et al. A Frequency Hopping Network Station Sorting Algorithm Based on Improved YOLOv8[J]. Acta Electronica Sinica, 2025, 53(02): 385-394. DOI：10.12263/DZXB.20240487

摘要

针对传统跳频网台分选技术在低信噪比条件下检测效果不佳且实时性差的问题，本文提出一种基于YOLOv8（You Only Look Once version 8）的跳频信号分选算法.首先，对接收到的混叠信号进行短时傅里叶变换生成灰度时频图作为YOLOv8网络模型的输入.其次，针对混叠信号中扫频、定频信号以及跳频信号之间发生频率碰撞对检测精度的影响，在C2f层中引入可变形卷积核（Deformable Convolutional Net-works v2，DCNv2）提高网络特征提取的泛化能力.再次，在Backbone层中加入SimAM注意力机制，解决低信噪比下背景噪声易与跳频信号混淆影响检测精度的问题.最后，将Detect检测头的卷积核替换为局部卷积核（Partial Convolution，PConv），在mAP@0.5精度损失不超过0.37%的情况下使网络计算复杂度降低32.18%，提高网络模型的推理速度.实验结果表明，本文所提算法在信噪比为-5 dB时分选率达到97.68%，且模型收敛快，鲁棒性强.

Abstract

Aiming at the problem that traditional frequency hopping network station sorting technology is ineffective under low signal-to-noise ratio conditions and has poor real-time detection performance

this paper proposes a shortwave frequency hop-ping signal sorting algorithm based on the improved YOLOv8 (You Only Look Once version 8). First

the short-time Fourier transform is performed on the received aliasing signal to generate a grayscale time-frequency image as the input of the YOLOv8 network model. Secondly

in view of the impact of frequency collisions between aliasing signals such as sweep frequency signals

fixed frequency signals and frequency hopping signals on detection accuracy

the Deformable Convolutional Net-works v2 is introduced in the C2f layer to improve the generalization ability of network feature extraction. Thirdly

the Simam attention mechanism is added to the backbone layer to solve the problem that background noise is easily confused with frequency hopping signals and affects detection accuracy under low signal-to-noise ratio. Finally

the convolutional kernel of Detect module is replaced by Partial Convolution kernel

which reduces the computational complexity of the network by 32.18% without the accuracy loss of mAP@0.5 exceeding 0.37%

and improve the inference speed of the network model. Experimental results show that the improved YOLOv8 algorithm proposed in this paper has a separation rate of 97.68% at -5 dB signal-to-noise ratio

and the model has fast convergence and strong robustness.

关键词

Keywords

references

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