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SAR image formation algorithms have implicit or explicit dependence on the mathematical model of the image observation process. Inaccuracies in the image model will bring phase error, which may cause various quality degradations in the reconstructed images, especially in the millimeter-wave or terahertz-waves radar. In this paper, we propose a sparse Bayesian approach for joint SAR imaging and phase...
Compressive sensing (CS) has been successfully used in synthetic aperture radar (SAR) imaging and shows the great potential. However, the existing CS-based SAR models assume the exact mathematical model of the observation process. In practice, the inaccuracy in the observation model will cause various degradation in the reconstructed SAR images, especially in the frequencies of millimeter-wave or...
The compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the sparse reconstruction based on l1 norm is sensitive to the regularized factor and makes it inconvenient to be used in practice, the sparse Bayesian learning (SBL) is considered in this situation, which retains a preferable property of the l0 norm and has no user parameter. In this...
In this paper we develop an efficient anti-noise imaging algorithm based on Bayesian Compressive Sensing (BCS) theory. Random sampling is applied in range and azimuth direction respectively, and sparse dictionary matrixes are designed independently in each direction according to imaging geometry model. At last, BCS theory is used to reconstruct SAR image. BCS theory takes the prior knowledge of targets...
This paper presents a method for imaging of moving targets via the compress sensing by treating the imaging as a problem of signal representation in an over-complete dictionary. The essential idea behind sparse signal representation models comes from the fact that SAR ground moving targets are sparsely distributed in the observation scene and the received SAR echo is decomposed into the sum of basis...
The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser...
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