Yuexin Gao, Xinyu Zhang, M. Xing, Jixiang Fu, Zi-jing Zhang, Ying Wang
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ISAR Imaging Based on Homotopy Re-Weighted ℓ1-Norm Minimization
A suitable regularization parameter plays an important role in sparse ISAR imaging algorithms. With a proper regularization parameter, the quality of ISAR images improves. In this paper, the Homotopy re-weighted ℓ1-norm minimization is applied to ISAR imaging. This method is able to choose the accurate regularization parameter for each point in ISAR image with high efficiency. As a result, the imaging results processed by this method contain more details of the target and less artificial points. Both simulated and real data experiments validate the feasibility of the proposed method.
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