High-Resolution ISAR Imaging of Maneuvering Targets Based on 2-D Complex Fast Sparse Bayesian Learning

The maneuvering of the targets will induce time-varying Doppler during observation, posing great challenges for well-focused inverse synthetic aperture radar (ISAR) imaging. Furthermore, ISAR may encounter complex observation conditions such as incomplete data and low signal-to-noise ratio (SNR), which render the conventional maneuvering targets imaging methods invalid. To address these issues, this article proposes a novel high-resolution ISAR imaging method of maneuvering targets. First, the sparse imaging model of maneuvering targets is constructed by incorporating the rotation parameters into the observation dictionary. Then, the gamma-complex Gaussian prior is assigned to the ISAR image to exploit its sparse nature. On this basis, to circumvent the matrix inversion embedded in the traditional sparse Bayesian learning (SBL) method, the model lower bound is relaxed and a novel algorithm is proposed for efficient ISAR image reconstruction, dubbed 2-D complex fast SBL (2D-CFSBL). Furthermore, the maximum likelihood estimation is utilized to estimate the rotation parameters accurately. Finally, ISAR image reconstruction and rotation parameters estimation are performed iteratively to obtain well-focused image. Experimental results have validated the effectiveness and superiority of the proposed method under incomplete data and low SNR conditions.