We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering centers. Then, the model parameters and phase errors are estimated iteratively by expectation propagation (EP) and maximum likelihood (ML) estimation. Compared with the available imaging methods based on the numerical optimization and Bayesian inference, the proposed method has exhibited more flexibility in data representation and better performance in parameter estimation, particularly in sparse-aperture and low SNR scenarios.

中文翻译：

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基于期望传播的低信噪比环境下的高分辨率雷达成像

我们在近似贝叶斯推理框架中解决低信噪比 (SNR) 环境中高分辨率雷达成像的问题。首先，概率图形模型是通过在散射中心分布之前施加促进稀疏的尖刺和板坯来构建的。然后，通过期望传播（EP）和最大似然（ML）估计迭代地估计模型参数和相位误差。与现有的基于数值优化和贝叶斯推理的成像方法相比，所提出的方法在数据表示方面表现出更大的灵活性和更好的参数估计性能，特别是在稀疏孔径和低信噪比的情况下。