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.

Yuanhao Zheng

Jiantao Wang

Jie Huang

IET Radar Sonar & Navigation

IET Radar, Sonar and Navigation

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

Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space and has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.

Category	Quartile
管理学	4
管理学, 工程电子与电气	4
管理学, 电信学	4

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

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