An active jamming recognition algorithm based on ER-C-L network model
To solve the problem of low recognition accuracy of radar active jamming in strong noise environment,an algorithm for ER-C-L(Extended ResNet-CNN-LSTM)network model based on one-dimensional composite features is pro-posed.Firstly,the amplitude,instantaneous frequency,instantaneous envelope of power spectrum and their composite fea-tures are taken as network input to compare their recognition accuracy in ResNet-CNN model.The composite features of am-plitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features.Then,the complex features are injammed into the ER-C-L network to identify six new active jamming models.Simulation experiments show that the recognition accuracy of jamming is 98.5%in strong noise environment within the JNR of-10 dB.Compared with other deep learning algorithms such as CNN,ResNet-CNN,extended ResNet-CNN and LSTM,it has higher interference recognition accuracy.
active jamming recognitionconvolutional neural networkscompound feature