雷达科学与技术2026，Vol.24Issue(2)：196-204,9.DOI:10.3969/j.issn.1672-2337.2026.02.010
基于CNN-BiLSTM的对ELINT系统杂乱脉冲干扰抑制方法
Method for Suppressing Chaotic Pulse Interference in ELINT System Based on CNN-BiLSTM
摘要
Abstract
In response to the serious damage to the sorting and recognition of radar signals by the electronic intelli-gence(ELINT)system caused by chaotic pulse interference signals,a method based on convolutional neural network-bidirectional long short-term memory network for suppressing chaotic pulse interference is proposed by intro-ducing the machine learning algorithm into the reconnaissance sorting process.This method utilizes the convolutional neural network to capture the locally distributed features of chaotic pulse interference signals in multiple dimensions,and bidirectional long short-term memory network to capture the periodic features of radar signals within the mixed sig-nals,thereby achieving classification and identification of interference signals and radar signals.Simulation experiments have shown that this method achieves an accuracy rate of over 96%and a recall rate of over 92%for different test sets.It has strong domain generalization ability and provides an effective way to improve the performance of ELINT systems in complex electromagnetic environments.关键词
电子情报系统/卷积神经网络/双向长短期记忆网络/杂乱脉冲干扰/雷达侦察Key words
electronic intelligence(ELINT)system/convolutional neural network/bidirectional long short-term memory network/chaotic pulse interference/radar reconnaissance分类
信息技术与安全科学引用本文复制引用
鲁永为,师俊朋,周青松,田西兰,郭柏炀,陈沁娴,山世浩,王明壮..基于CNN-BiLSTM的对ELINT系统杂乱脉冲干扰抑制方法[J].雷达科学与技术,2026,24(2):196-204,9.基金项目
国家自然科学基金(62301581,62401600) （62301581,62401600）
中国博士后科学基金面上项目(2023M734313) （2023M734313）
