现代雷达2024，Vol.46Issue(6)：79-84,6.DOI:10.16592/j.cnki.1004-7859.2024.06.013
基于轻量化卷积神经网络的雷达干扰识别技术研究
Radar Interference Recognition Based on Lightweight Convolutional Neural Network
张海舟 1贺青 1马泽强 1黄亮 1李宗阳1
作者信息
- 1. 南京电子技术研究所,江苏 南京 210039
- 折叠
摘要
Abstract
With the advancement of technology,the current electronic warfare landscape has become increasingly complex.Radar systems are confronted with electronic interference characterized by high coherence,strong deception,stealthiness and low power.This significantly degrades their detection and tracking capabilities,potentially rendering them combat ineffective.Therefore,the accurate identification of the types of active interference faced by radars is crucial for implementing targeted interference suppres-sion.Lightweight convolutional neural networks(MobileNet),which can effectively capture spatial structural information in images without manual feature extraction,have exhibited excellent performance in image processing and classification.In this paper,a ra-dar interference identification model is proposed based on MobileNet,which is validated by a dataset of time-frequency characteris-tics of radar active interference.Experimental results reveal that the established model achieves a high F1-score of approximately 0.9 for radar interference identification and classification,outperforming models such as SIFT template matching and CNN in vari-ous metrics,thereby demonstrating superior classification performance.关键词
雷达/有源干扰/轻量化卷积神经网络/分类Key words
radar/active interference/lightweight convolutional neural network(MobileNet)/classification分类
信息技术与安全科学引用本文复制引用
张海舟,贺青,马泽强,黄亮,李宗阳..基于轻量化卷积神经网络的雷达干扰识别技术研究[J].现代雷达,2024,46(6):79-84,6.
