2D Superresolution ISAR Imaging via Temporally Correlated Multiple Sparse Bayesian Learning

  • Xiaowei Hu
  • Ningning Tong
  • Xingyu He
  • Yuchen Wang
Research Article


In inverse synthetic aperture radar (ISAR) imaging, the image resolution is always limited by the bandwidth and the observation time. Sparse recovery (SR) is recently proposed to improve the range resolution or cross-range resolution effectively. However, for the two dimensional superresolution case, a SR-induced range cell migration (RCM) occurs among the High-Resolution Range Profiles (HRRPs) and definitely degrades the ISAR image. After that translational motion compensation is completed, the common sparsity of HRRPs is exploited to suppress the RCM in this paper. Furthermore, by taking the temporal correlation of HRRPs into account, an ISAR imaging method based on temporally correlated Multiple Sparse Bayesian Learning is proposed to improve the imaging quality. Simulated data and real data results demonstrate the effectiveness of the proposed method.


Inverse synthetic aperture radar 2D superresolution Sparse recovery Temporally correlated Multiple Sparse Bayesian Learning 



This work was supported by the National Natural Science Foundation of China (Nos. 61701526, 61372166, 61571459).


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Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  • Xiaowei Hu
    • 1
  • Ningning Tong
    • 1
  • Xingyu He
    • 1
  • Yuchen Wang
    • 1
  1. 1.Air Force Engineering UniversityXi’anChina

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