SAR imaging of multiple maritime moving targets based on sparsity Bayesian learning

Imaging of multiple maritime moving targets is a challenging task in the synthetic aperture radar (SAR) system owing to the fact that the complex target motion produces evident image defocusing. Given that the sparsity of ship targets in the SAR image, the authors propose a new imaging method of multiple maritime moving targets based on sparsity Bayesian learning. To avoid the overcomplete velocity dictionary with heavy computational burden, the Gaussian chirplet transform is exploited and improved based on the velocity constraint to estimate target Doppler parameters for the observation matrix construction. An observation model is established for imaging multiple ship targets. In the proposed method, the multiple ship target imaging task is formulated into the sparsity Bayesian framework, which provides a posterior density function for the target image and improves the imaging quality over the conventional methods based on the point estimate. The Bayesian compressive sensing (BCS) using a hierarchical form of the Laplace prior is applied to reconstruct the moving target image. Since BCS provides an estimation of the uncertainty in the reconstruction, the sea clutter can be well suppressed while the multiple targets are refocused. Simulations and experimental Gaofen-3 data are performed to verify the effectiveness of the proposed method.