Intelligent Detection and Identification of Broadband Signals Based on Time-frequency Map Cutting

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Intelligent Detection and Identification of Broadband Signals Based on Time-frequency Map Cutting

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[1]HAN Gangtao,MA uipeng,WU Di.Intelligent Detection and Identification of Broadband Signals Based on Time-frequency Map Cutting[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):44-51.[doi:10.13705/j.issn.1671-6833.2023.03.008]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 Number of periods: 2023 03 Page number: 44-51 Column: Public date: 2023-04-30

Title:: Intelligent Detection and Identification of Broadband Signals Based on Time-frequency Map Cutting

Author(s):: HAN Gangtao¹; MA Ｒuipeng²; WU Di³; 1.School of Electrical and Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, 2.School of Network Space Security, Zhengzhou University, 450002, Zhengzhou, Henan,3. School of Strategic Support for the Chinese People’s Liberation Army, School of Information Engineering Engineering Engineering, Henan Zhengzhou 450001

Keywords:: signal detection; deep learning; large bandwidth; lightweight; Focal-EIOU

CLC:: TN971. 1

DOI:: 10.13705/j.issn.1671-6833.2023.03.008

Abstract:: To address the problem of efficient detection of noncooperative communication signals in large bandwidth scenarios, an intelligent detection and identification method for broadband signals based on time-frequency map cutting was proposed in combination with machine learning techniques. In this study, the method adopted Mobilenet network instead of CSPdarknet53 network in YOLOv4 for feature extraction, and constructed a lightweight YOLOv4 model. At the same time, the model introduced the Focal-EIOU cost function and an improved weighted box fusion algorithm (WBF), which could effectively improve the training efficiency and the detection and recognition accuracy. The experimental results showed that the method in this study could quickly detect the continuous and burst signals in the communication acquisition data with large bandwidth, as well as the moment of appearance, frequency range, modulation mode and other related parameters, and its performance is better than the traditional energy detection methods. Compared with other similar methods, the average detection accuracy (mAP) of this study was greater than 81%, and the detection speed of YOLOv4-MobilenetV1 model reached 77.60 frames/s, which was better for both detection accuracy and real-time requirements and was more conducive to engineering deployment.

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Last Update: 2023-05-08