Radar signals recognition based on attention and denoising residual network

¹ Jiangnan University, School of Internet of Artificial Intelligence and Computer, 214122 Wuxi China
² Huaiyin Institute of Technology, Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, 223003 Huaian China

^* Corresponding author: Lya_wx@jiangnan.edu.cn

Abstract

To solve the problem that complex radar emitter signals are difficult to identify under low signal-to-noise ratio, this paper proposes a novel radar signal recognition method based on an improved deep residual network. In this method, two IQ signals are used as the input of the method, which saves time for generating time-frequency images, and then the signal features are extracted through an improved deep residual network. A nonlinear transform layer is inserted into the network to automatically confirm the threshold value, and then the soft threshold method is used to denoise. The importance of features is weighted by attention unit, and then classified by softmax classifier. The experiments based on five kinds of radar signal datasets show higher accuracy at low signal-to-noise ratio compared with other methods. The experiments also verified its overall accuracy can still exceed 90% even at extremely low signal-to-noise ratio of -16dB.