Modulation Recognition of Frequency Hopping Signal under Interference Based on Improved YOLOv5s

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Modulation Recognition of Frequency Hopping Signal under Interference Based on Improved YOLOv5s

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[1]ZHANG Haibin,WEI Hongji,WANG Chao,et al.Modulation Recognition of Frequency Hopping Signal under Interference Based on Improved YOLOv5s[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):43-50.[doi:10.13705/j.issn.1671-6833.2025.05.008]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 43-50 Column: Public date: 2025-08-10

Title:: Modulation Recognition of Frequency Hopping Signal under Interference Based on Improved YOLOv5s

Author(s):: ZHANG Haibin¹; WEI Hongji¹; WANG Chao2; XIANG Changbo³; YANG Mingyang³; LI Xiaolong⁴; 1.Hangzhou Research Institute,Xidian University, Hangzhou 311200, China; 2.School of Telecommunications Engineering, Xidian University, Xi’an 710126, China; 3.Ceyear Technologies Co., Ltd., Qingdao 266555, China; 4.Beijing Institute of Control and Electronic Technology, Beijing 100038, China

Keywords:: frequency hopping signal; signal detection; signal recognition; interference signal; YOLOv5s

CLC:: TN971

DOI:: 10.13705/j.issn.1671-6833.2025.05.008

Abstract:: In complex electromagnetic environments, interference signals can severely degrade the detection and recognition performance of frequency-hopping signals. To address issues of false detection, missed detection, and over-detection in traditional methods, in this study an improved time-frequency diagram-based signal detection and recognition algorithm was proposed by modifying the YOLOv5s network. Firstly, a composite dataset containing frequency hopping signals + interference signals was constructed, comprising 4 modulation types of frequency hopping signals and 6 interference types, with 300 high-resolution time-frequency diagram samples generated for each combination (totaling 7 200 groups). Secondly, considering the similar features between interference and signals in time-frequency diagrams, and recognizing that the frequency variation pattern of hopping signals could make background information around signals crucial for differentiation, a context hierarchy module was proposed to hierarchically process background information. This module employed depthwise separable convolution to extract surrounding background features and utilized a gated aggregation mechanism to perform weighted fusion of background information and signal features, thereby generating more discriminative composite features. Finally, the backbone network of YOLOv5s was modified by integrating the context hierarchy module and gated aggregation mechanism to develop an improved frequency hopping signal detector. Simulation results showed that compared with the original YOLOv5s network, the proposed method achieved 15.9 percentage points improvement in recall rate R, 8.9 percentage points enhancement in mean average precision mAP@0.5∶0.95, and 9 percentage points increase in F1, while significantly reducing false and missed detection occurrences.

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Last Update: 2025-09-19