Open space radar specific emitter identification using MSAK-CNN-LSTM network

Abstract

To enhance the capability of identifying unknown emitters in open spaces, an open-multiscale attention kernel (MSAK)-convolutional neural network-long short-term memory (CNN-LSTM) structure is proposed. To this end, first, a MSAK module and CNN-LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK-CNN-LSTM model is improved to obtain an open-MSAK-CNN-LSTM model with open-set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open-MSAK-CNN-LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal-to-noise ratio (SNR) scenarios.