Journal of Xidian University ›› 2019, Vol. 46 ›› Issue (5): 31-40.doi: 10.19665/j.issn1001-2400.2019.05.005

Previous Articles     Next Articles

Sparse representation of large dynamic range SAR imaging for multiple ground moving targets

YANG Lei1,YUE Yunze1,LI Pucheng1,ZHANG Tao1,YANG Huan2   

  1. 1. Tianjin Key Lab. for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300, China
    2. Institute of Electronic Engineering,China Academy of Engineering Physics,Mianyang 621999,China
  • Received:2019-01-07 Online:2019-10-20 Published:2019-10-30


When multiple ground moving targets are to be imaged simultaneously by a synthetic aperture radar, the dynamic range of the target responses in the SAR image will be reduced in terms of increased side-lobes. To this end, a parametric Bayesian learning algorithm is proposed in this paper for enhancing the sparse feature of the SAR image as well as reducing side-lobes of the target responses. First, the asymptotically linear Lv’s distribution as a novel time-frequency representation method is adopted to represent the Doppler parameters of the moving targets at the centroid frequency in the chirp rate domain. Accordingly, a quadratic Fourier dictionary is constructed for the following sparse Bayesian learning. Second, in order to evaluate the performance of the designated dictionary quantitatively, the mutual correlation among columns of the dictionary is calculated to evaluate the unaccessable restricted isometry property indirectly. Finally, by encoding a sparse prior or Laplacian distribution onto the multiple moving targets to be imaged, the Bayesian model is established in a hierarchical manner. Following variational Bayesian expectation maximization, the posterior of the target image can be analytically derived, and the sparse feature enhanced synthetic aperture radar image with a promising dynamic range in target response can be obtained. In addition, the non-systematic phase errors from both the airborne radar motion deviation and non-ideal target movement are considered within the Bayesian learning framework, which can therefore achieve promising results. The effectiveness of the proposed algorithm is validated by both simulations and raw data experiments, and the superiority is evaluated by comparing with conventional algorithms.

Key words: synthetic aperture radar, sparse representation, image reconstruction, moving target, cross-correlation

CLC Number: 

  • TN958