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Comparison of parametric sparse recovery methods for ISAR image formation

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Abstract

A parametric sparse representation model of the inverse synthetic aperture radar (ISAR) signal has been proposed recently, and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate. Based on the parametric sparse representation model, several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image. In this paper, four parametric sparse recovery algorithms are compared mainly in three aspects: the accuracy of the rotation rate estimation, the ISAR image quality and the computational load. Numerical examples are presented to show the advantages and disadvantages for each method.

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Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Wei Rao, Gang Li & XiQin Wang

  2. Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA

    XiangGen Xia

  3. Department of Electronic Engineering, Chonbuk National University, Jeonju, South Korea

    XiangGen Xia

Authors
  1. Wei Rao
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  2. Gang Li
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  3. XiQin Wang
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  4. XiangGen Xia
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Correspondence to Gang Li.

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Rao, W., Li, G., Wang, X. et al. Comparison of parametric sparse recovery methods for ISAR image formation. Sci. China Inf. Sci. 57, 1–12 (2014). https://doi.org/10.1007/s11432-013-4859-9

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  • DOI: https://doi.org/10.1007/s11432-013-4859-9

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