Multi‐scale group‐fusion convolutional neural network for high‐resolution range profile target recognition

Abstract

Abstract Convolution neural networks (CNNs) represent one of the workhorses of artificial intelligence applications. As a typical artificial intelligence application, a high‐resolution range profile (HRRP) target recognition method based on CNNs has aroused a lot of research interest. Most CNNs use a relatively small and single‐scale convolution kernel size to control the number of parameters and computational complexity, but recent studies indicate that CNNs with a small kernel size cannot extract enough spatial information, which hurts the recognition performance. Aiming at this problem, this paper proposes a multi‐scale group‐fusion one‐dimensional convolution neural network (MSGF‐1D‐CNN) for HRRP target recognition. MSGF‐1D‐CNN utilises multi‐scale group one‐dimensional convolution (MSG 1D‐Conv) and point‐wise convolution (PW‐Conv) to replace the standard convolution. Multi‐scale group one‐dimensional convolution can significantly reduce complexity and capture the information of targets within HRRP in different levels of detail to enhance feature extraction, while PW‐Conv can realise the fusion of multi‐scale features to help boosting recognition performance. Experiments on five mid‐course ballistic targets in the HRRP dataset show that MSGF‐1D‐CNN has superior recognition performance, and the parameter number of the model is reduced by more than 2.4 times than standard 1D‐CNN. Furthermore, MSGF‐1D‐CNN shows better performance on fine‐grained HRRP target recognition and anti‐noise robustness in most cases.

Keywords

• convolution neural networks
• feature fusion
• high‐resolution range profile
• multi‐scale group convolution
• mid‐course ballistic targets
• point‐wise convolution