Adaptive intra‐pulse interference waveform generation technique based on Convolutional Neural Network—Deep Neural Network with global priority information of radar signal

To adapt to the complex and changeable electromagnetic environment of radar detection, improve jamming effect on frequency-agile low probability intercept radar, solve problem poor caused by intra-pulse lagging behind target signal, a Convolutional Neural Network—Deep Network waveform generation method, based prior global information is presented in this study. This proposed method uses two networks generate overall interference waveforms. One derives from local information, which used other interference. In whole scheme, an algorithm design signal proposed. The can samples number area multiple false targets relative peak between real relevant parameters. Then, evaluation system (Matched filter, Constant False Alarm Rate Detector, Distance resolution etc.) established evaluate designed Finally, time-domain Root Mean Squared Errors as loss function optimise training network ultimately achieve requirement generating adaptive waveforms fragments. principle various aspects effect, enhances time domain correlation areas, thereby boosting radar. experimental results show that effectively lag small effective after pulse pressure matching filter. study offers certain reference significance for information.