SCIENCE CHINA Information Sciences, Volume 59, Issue 6: 062310(2016) https://doi.org/10.1007/s11432-015-5477-5

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

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  • ReceivedSep 7, 2015
  • AcceptedOct 20, 2015
  • PublishedApr 23, 2016


This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.

Funded by

Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT-14-B09)

Innovation of the Chinese Academy of Sciences International Partnership Project(Y313110240)

National Natural Science Foundation of China(61201433)

National Natural Science Foundation of China(61372186)



The work was supported by Innovation of the Chinese Academy of Sciences International Partnership Project (Grant No. Y313110240), National Natural Science Foundation of China (Grant Nos. 61201433, 61372186), and Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (Grant No. NJYT-14-B09).


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