Fast 2D super resolution ISAR imaging method under low signal‐to‐noise ratio. Issue 10 (7th September 2017)

• Record Type: Journal Article
• Authors: Li, Shaodong; Chen, Wenfeng; Liu, Weijian; Yang, Jun; Ma, Xiaoyan
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 10(2017)
• Page Start: 1495
• Page End: 1504
• Publication Date: 2017-09-07
• Subjects: synthetic aperture radar -- radar imaging -- radar resolution -- image resolution -- image representation -- Doppler radar -- communication complexity -- image reconstruction -- gradient methods -- optimisation -- matrix algebra; Nesterov accelerated gradient method -- soft threshold parameter -- adaptive‐adjustment strategy -- sensing matrix optimization -- computational complexity -- 2D complex‐valued dictionary -- range frequency‐azimuth Doppler domain -- 2D joint SR model -- 2D real‐valued dictionary -- CNLBI‐TRD algorithm -- two‐dimensional real‐valued dictionary -- coupled Nesterov linearised Bregman iteration algorithm -- image resolution -- inverse synthetic aperture radar -- sparse representation model -- signal‐to‐noise ratio -- fast 2D super resolution ISAR imaging method; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0033
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Sparse representation (SR)‐based inverse synthetic aperture radar (ISAR) imaging method can achieve super‐resolution image of a target. However, it is computationally expensive and sensitive to noise. To overcome these two drawbacks, the authors propose the coupled Nesterov linearised Bregman iteration algorithm based on two–dimensional (2D) real‐valued dictionaries (CNLBI‐TRD) for ISAR imaging. First, the ISAR echoes are taken as a 2D joint SR model in the range frequency‐azimuth Doppler domain. Then the 2D complex‐valued dictionaries are converted into real‐valued ones through unitary transformations. The computational complexity is thus decreased by a factor of at least four. Finally, the CNLBI algorithm is proposed to reconstruct the 2D SR model directly. It combines the Nesterov's accelerated gradient method with the condition number optimisation of sensing matrices. An adaptive‐adjustment strategy of the soft threshold parameter is presented. Thus the total iteration numbers can be greatly reduced. The simulation results and real data experiments verify the effectiveness of the proposed imaging algorithm.