In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed.

中文翻译：

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基于压缩感知的联合距离多普勒和杂波估计

在本文中，我们解决了存在杂波的雷达距离多普勒成像问题。具体来说，我们将距离多普勒成像问题表述为恢复受杂波和噪声污染的稀疏矢量的问题。我们提出了一种基于稀疏贝叶斯学习（SBL）的算法来联合获取距离多普勒图像、噪声方差和杂波协方差矩阵。此外，我们采用了一种简单的剪枝机制来降低计算成本并提高收敛速度。