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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 17
• Main Authors: Wang, Yuanyuan, Dai, Fengzhou, Liu, Qian, Liang, Yi, Lu, Xiaofei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 2-D joint super-resolution inverse synthetic aperture radar (ISAR) imaging; Algorithms; Bayesian analysis; Covariance matrix; Decomposition; Doppler sonar; Fast Fourier transformations; fast sparse Bayesian learning (SBL) and iterative Wiener filter (IWF) algorithms; Fourier transforms; fourth-order Toeplitz tensor; Image processing; Image reconstruction; Image resolution; Imaging; Inverse synthetic aperture radar; Lobes; Matching pursuit algorithms; multidwell joint coherent processing; Phased arrays; Probability theory; Radar; Radar arrays; Radar equipment; Radar imaging; Radar transmission; range spatial-variant autofocus; Rotation; SAR (radar); Signal processing algorithms; Superresolution; Tensors; Wiener filtering
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3450705

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.