Individual identification for a radar emitter combined with attention mechanism and CNN

DOI:	10.11809/bqzbgcxb2023.04.041
Keywords:	attention mechanism; convolutional neural network; individual identification of radar emitter; bispectrum analysis
Abstract:	Aiming at the problems of low recognition accuracy, poor anti noise performance and long training time of individual identification for a radar emitter, this paper proposes a method combining attention mechanism and convolutional neural network. Firstly, the system model of a radar emitter is established according to the hardware difference of a radar transmitter power amplifier. Secondly, the radar signal is analyzed by bispectrum, and the obtained bispectrum image is used as the input of the network. Then, the attention mechanism is introduced into the optimized convolutional neural network to improve the ability of individual identification. Finally, compared with the existing methods, the effectiveness of the algorithm is verified. The experimental results show that, compared with the convolutional neural network, the identification accuracy of the proposed method increases by 5% and the training time cuts in half.
Issue:	Vol. 44 No. 4 (2023)
Published:	2023-04-28
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