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      Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration

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          Abstract

          This study aims to enable steady and speedy acquisition of Inverse Synthetic Aperture Radar (ISAR) images using sparse echo data. To this end, a Multiple Measurement Vectors (MMV) ISAR echo model is studied. This model is then combined with the Compressive Sensing (CS) theory to realize a class of MMV fast ISAR imaging algorithms based on the Linearized Bregman Iteration (LBI). The algorithms involve four methods, and the iterative framework, application conditions, and relationship between the four methods are given. The reconstructed performance of the methods, convergence, anti-noise, and selection of regularization parameters are then compared and analyzed comprehensively. Finally, the experimental results are compared with the traditional Single Measurement Vector (SMV) ISAR imaging algorithm; this comparison shows that the proposed algorithm delivers an improved imaging quality with a low Signal-to-Noise Ratio (SNR).

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 August 2016
          : 5
          : 4
          : 389-401
          Affiliations
          [1 ] Air Force Early Warning Academy, Wuhan 430019, China
          Article
          6f39cfefd3cc4ed48fa9602edd83126d
          10.12000/JR16057

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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