Joint Translational Motion Compensation and High-Resolution ISAR Imaging Based on Sparse Bayesian Learning

Accurate translational motion compensation is crucial to high-resolution inverse synthetic aperture radar (ISAR) imaging, while complex observation environments generally render conventional translational motion compensation and imaging methods invalid. To tackle this issue, a novel method for joint translational motion compensation and high-resolution imaging is proposed within the Bayesian framework. Firstly, the statistical model is established, in which the gamma process-Laplace (GaPL) nonparametric prior is proposed to attain flexible sparse representation. Then, to address the issue of matrix inversion in conventional Bayesian inference, a 2D inverse-free Bayesian imaging method with the GaPL prior, namely 2D-IFGaPL, is proposed, which significantly improves the computational efficiency. After that, a method for joint range offsets and phase errors estimation is proposed to achieve accurate translational motion compensation. Finally, sparse Bayesian imaging and translational motion compensation are alternatively implemented until convergence to generate well-focused image. Experimental results under complex observation environments, e.g., low signal-to-noise ratio (SNR) and incomplete data, have demonstrated the effectiveness of the proposed method.