Inverse synthetic aperture radar imaging using complex‐value deep neural network. Issue 20 (18th September 2019)

• Record Type: Journal Article
• Authors: Hu, ChangYu; Wang, Ling; Li, Ze; Sun, Lingling; Loffeld, Otmar
• Is Part Of: Journal of engineering. Volume 2019:Issue 20(2019)
• Page Start: 7096
• Page End: 7099
• Publication Date: 2019-09-18
• Subjects: radar imaging -- synthetic aperture radar -- learning (artificial intelligence) -- compressed sensing -- neural nets -- image reconstruction -- image classification; complex‐value deep neural network -- traditional ISAR imaging methods -- compressive sensing‐based imaging methods -- high‐quality images -- sparse representation -- target scene -- relatively low computational efficiency -- image reconstruction algorithms -- CS‐based ISAR imaging methods -- deep learning technology -- image classification -- multiple hidden layers -- nonlinear activation layer -- novel ISAR imaging method -- image formation -- target image -- state‐of‐the‐art CS reconstruction algorithms -- imaging efficiency -- inverse synthetic aperture radar; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0571
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : As compared with traditional ISAR imaging methods, the compressive sensing (CS)‐based imaging methods can obtain high‐quality images using much less under‐sampled data. However, the availability or appropriateness of the sparse representation of the target scene and the relatively low computational efficiency of image reconstruction algorithms limit the performance and application of the CS‐based ISAR imaging methods. In recent years, the deep learning technology has been applied in many fields and achieved outstanding performance in image classification, image reconstruction etc. DL implements the tasks using the deep neural network (DNN), which composes multiple hidden layers and non‐linear activation layer. In this study, a novel ISAR imaging method that uses a complex‐value deep neural network (CV‐DNN) to perform the image formation using under‐sampled data is proposed. The CV‐DNN architecture can extract and exploit the sparse feature of the target image extremely well by multilayer non‐linear processing. The experimental results show that the proposed CV‐DNN‐based ISAR imaging method can provide better shape reconstruction of target with less data than state‐of‐the‐art CS reconstruction algorithms and improve the imaging efficiency obviously.