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.

中文翻译：

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基于稀疏贝叶斯学习的多个海上运动目标SAR成像

由于复杂的目标运动会产生明显的图像散焦，因此在合成孔径雷达（SAR）系统中，对多个海上移动目标进行成像是一项艰巨的任务。鉴于SAR图像中舰船目标的稀疏性，作者提出了一种基于稀疏贝叶斯学习的多种海上运动目标成像方法。为了避免计算字典过于繁琐，运算量大，基于速度约束对高斯Chirplet变换进行了开发和改进，以估计目标多普勒参数以建立观测矩阵。建立了用于对多个舰船目标成像的观察模型。在提出的方法中，将多舰目标成像任务制定为稀疏贝叶斯框架，与传统的基于点估计的方法相比，它提供了目标图像的后密度功能，并提高了成像质量。使用拉普拉斯先验的分层形式的贝叶斯压缩感测（BCS）应用于重构运动目标图像。由于BCS提供了重建中不确定性的估计，因此在重新聚焦多个目标时，可以很好地抑制海浪杂波。仿真和实验高芬3数据进行了验证该方法的有效性。由于BCS提供了重建中不确定性的估计，因此在重新聚焦多个目标时，可以很好地抑制海浪杂波。仿真和实验高芬3数据进行了验证该方法的有效性。由于BCS提供了重建中不确定性的估计，因此在重新聚焦多个目标时，可以很好地抑制海浪杂波。仿真和实验高芬3数据进行了验证该方法的有效性。