基于CNN-BiLSTM的对ELINT系统杂乱脉冲干扰抑制方法OA

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.