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Li Gang, Xia Xiang-Gen. Parametric Sparse Representation and Its Applications to Radar Sensing[J]. Journal of Radars, 2016, 5(1): 1-7. doi: 10.12000/JR15126
Citation: Li Gang, Xia Xiang-Gen. Parametric Sparse Representation and Its Applications to Radar Sensing[J]. Journal of Radars, 2016, 5(1): 1-7. doi: 10.12000/JR15126
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Parametric Sparse Representation and Its Applications to Radar Sensing

doi: 10.12000/JR15126
  • 1.

    (Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)

  • 2.

    (Department of Electrical & Computer Engineering, University of Delaware, Newark, DE 19716, USA)

Funds:

The National Natural Science Foundation of China (61422110, 41271011), The National Ten Thousand Talent Program-Young Top-Notch Talent Program, The Tsinghua University Initiative Scientific Research Program, The Tsinghua National Laboratory for Information Science and Technology (TNList) Program

  • Received Date: 2015-12-14
  • Rev Recd Date: 2016-01-06
  • Publish Date: 2016-02-28
    Abstract
  • Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.

     

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