Fast SBL for 2-D Joint Super-Resolution ISAR Imaging With Multidwell Observation Based on LC Decomposition of Fourth-Order Toeplitz Tensor

For multifunctional phased array radar systems, inverse synthetic aperture radar (ISAR) imaging typically requires multidwell coherent processing to achieve adequate cross-range resolution. In such cases, the traditional fast Fourier transform (FFT)-based Doppler processing method can result in grating lobes in the cross-range dimension. Additionally, the range resolution is insufficient when the radar transmission bandwidth is relatively narrow. To address these two issues, this article proposes a fast sparse Bayesian learning (SBL)/iterative Wiener filter (IWF)-based 2-D joint super-resolution ISAR imaging method, which enhances the resolution in both range and cross-range dimensions while suppressing grating lobes. The proposed fast SBL and IWF algorithms incorporate the lower-triangular-Toeplitz-cyclic (LC) decomposition of the unfolded fourth-order Toeplitz tensor of the covariance matrix. Except for the LC decomposition, all operations utilize the FFT, allowing for efficient, precise, and memory-efficient processing of two types of multidwell observations. Furthermore, during image reconstruction, the target’s rotational velocity is estimated using the minimum entropy criterion, thereby achieving range-variant autofocus and 2-D super-resolution ISAR imaging. Finally, simulated and measured data are employed to validate the effectiveness of the proposed algorithms.