Open AccessRadar HRRP recognition based on CNN
Author(s) -
Song Jia,
Wang Yanhua,
Chen Wei,
Li Yang,
Wang Junfu
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0725
Subject(s) - computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , feature extraction , channel (broadcasting) , visualization , automatic target recognition , range (aeronautics) , synthetic aperture radar , engineering , computer network , philosophy , linguistics , aerospace engineering
In this study, ground target recognition based on one‐dimensional convolutional neural network (CNN) is studied by exploiting the targets’ high‐resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially, CNN can automatically discover features for classification. The authors propose a multi‐channel CNN architecture that can be applied on diverse forms of HRRP such as amplitude, complex, spectrum etc. Experimental results demonstrate the superiorities of the proposed method over conventional methods based on handcrafted features and single‐channel CNN in terms of recognition accuracy. Visualisation of the ‘deep features’ shows higher separability than handcrafted features, thus providing an insight into its effectiveness in exploiting the intrinsic structures.