The variable signal patterns and extremely high data rate of phased array radar greatly increase the complexity of electromagnetic environment, which makes the traditional method of radar working mode identification face great challenges. In this paper, a network structure based on temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM) parallel fusion processing is proposed. Depending on the advantages of TCN in depth temporal feature extraction and Bi-LSTM in global time series feature extraction, the typical working mode of phased array radar is accurately recognized. The experimental results show that under the condition of complex parameter interleaving, the recognition accuracy of the network for typical operating modes of phased array radar reaches 96.77%, which proves the feasibility of the method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.