Journal of Xidian University ›› 2019, Vol. 46 ›› Issue (3): 123-129.doi: 10.19665/j.issn1001-2400.2019.03.019

Previous Articles     Next Articles

Sparse-aperture ISAR imaging algorithm

ZENG Chuangzhan1,ZHU Weigang2,JIA Xin2   

  1. 1. Graduate School, Space Engineering Univ., Beijing 101416, China
    2. Dept. of Electronic and Optical Engineering, Space Engineering Univ., Beijing 101416, China
  • Received:2018-09-20 Online:2019-06-20 Published:2019-06-19


Under sparse aperture conditions, some problems arise with inverse synthetic aperture radar imaging such as low azimuth resolution and susceptibility to noise. To solve them, the two-dimensional sparseness of a target is used to transform the imaging problem into the sparse signal reconstruction problem under the multiple measurement vectors model. The zero norm-least mean square algorithm is processed in parallel to improve the efficiency. The optimal step-size formula is used instead of the fixed step-size to avoid the influence of the improper step-size on the convergence speed and accuracy. And the smoothed zero norm is introduced to approximate the zero norm to improve the reconstruction accuracy and noise immunity ability. In comparison with existing methods, the proposed algorithm can obtain a clearer target image, which is robust to noise and requires less computation. The effectiveness of the proposed method is verified by simulation and the real data processing result.

Key words: inverse synthetic aperture radar, compressed sensing, sparse aperture, adaptive filtering, smoothed zero norm, multiple measurement vectors

CLC Number: 

  • TN95