In inverse synthetic aperture radar (ISAR) imaging, the image resolution is always limited by the bandwidth and the observation time. Sparse recovery (SR) is recently proposed to improve the range resolution or cross-range resolution effectively. However, for the two dimensional superresolution case, a SR-induced range cell migration (RCM) occurs among the High-Resolution Range Profiles (HRRPs) and definitely degrades the ISAR image. After that translational motion compensation is completed, the common sparsity of HRRPs is exploited to suppress the RCM in this paper. Furthermore, by taking the temporal correlation of HRRPs into account, an ISAR imaging method based on temporally correlated Multiple Sparse Bayesian Learning is proposed to improve the imaging quality. Simulated data and real data results demonstrate the effectiveness of the proposed method.
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This work was supported by the National Natural Science Foundation of China (Nos. 61701526, 61372166, 61571459).
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