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In this paper, a novel autofocus imaging method is proposed to achieve high-resolution for inverse synthetic aperture radar (ISAR) in the compressive sensing (CS) framework. Firstly, we fomulate the ISAR CS imaging in a Multiple Measurement Vector (MMV) sparse optimization problem. Then, by utilizing the structure sparsity of ISAR image, i.e. row sparsity and column sparsity simutaneously, our method...
A novel high-resolution inverse synthetic aperture radar (ISAR) imaging method using homotopic non-convex regularization technique is proposed in this paper. Compared with exsiting compressive sensing (CS) based imaging methods, the proposed method can deal well with off-grid scatterers in the imaging scene, besides it has low computational complexity, therefore has potential to be applied in practice...
Most existing compressed sensing (CS) based radar imaging methods are based on the assumption that the targets are sparse enough, while in practice the targets are often spatially extended, which would degrade their inversion performances severely. In fact, the concentrations of the corresponding strong scatterers always form certain regions in high-resolution radar. Therefore, there still exist dependence...
A new fusion method of radar data and IFF data based on nonnegative matrix factorization (NMF) is proposed in this paper, due to its strong part-based representation capability. The identification data from each sensor are put into one column of the input matrix and the fusion is realized by the converging process of a cost function. The proposed fusion method does not only show better performance...
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