现代防御技术2025，Vol.53Issue(2)：91-98,8.DOI:10.3969/j.issn.1009-086x.2025.02.010
基于改进轻量化神经网络的干扰识别方法
Radar Interference Recognition Based on Improved Lightweight Convolutional Neural Networks
付亦凡 1阮航 1周东平 1穆贺强1
作者信息
- 1. 北京无线电测量研究所,北京 100854
- 折叠
摘要
Abstract
Considering a large number of radar interference signals could be built within a short period on the battlefield.Traditional convolutional neural networks face issues due to their large scale.It's a challenge to deploy jamming recognition system on small-scale equipment.This paper proposes an improved lightweight convolutional neural network to solve the problem by adopting adaptive kernel and batch normalization technology to improve recognition efficiency.By extracting time-frequency character to construct training and testing database for neural network training.Experiment shows that the network achieves over 96%identification accuracy for six kinds of interference signals under-8dB JNR.Compared with other networks,it has a superior accuracy efficiency ratio.关键词
雷达有源干扰/卷积神经网络/轻量化/动态卷积核/特征提取Key words
radar active jamming/convolutional neural network/lightweight/adaptive kernel/feature extraction分类
信息技术与安全科学引用本文复制引用
付亦凡,阮航,周东平,穆贺强..基于改进轻量化神经网络的干扰识别方法[J].现代防御技术,2025,53(2):91-98,8.
