We consider a regularized Maximum Likelihood Estimation (MLE) framework to produce images in the context of radio interferometric measurements. Specifically, we consider the class of compound Gaussian distributions to model the additive noise in the presence of radiofrequency interferences. In most cases, direct maximization of the likelihood is not tractable. To overcome this issue, we propose a generic expectation–maximization (EM) algorithm in the presence of a compound Gaussian noise. In addition, we leverage an approximation of the forward radio interferometric operator to derive an original latent data space that allows the use of the FFT in the maximization step, leading to an accelerated extension of the proposed imaging algorithm. The proposed approaches are evaluated on simulated and real data and show a significant improvement in the robustness to the presence of radiofrequency interferences (RFI) in the measurement.

中文翻译：

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存在射频干扰时无线电干涉成像的正则最大似然估计

我们考虑使用正则化最大似然估计（MLE）框架来在无线电干涉测量的背景下生成图像。具体来说，我们考虑复合高斯分布类别来对存在射频干扰的加性噪声​​进行建模。在大多数情况下，直接最大化可能性并不容易处理。为了克服这个问题，我们提出了一种在存在复合高斯噪声的情况下的通用期望最大化（EM）算法。此外，我们利用前向无线电干涉算子的近似来导出原始潜在数据空间，允许在最大化步骤中使用 FFT，从而加速扩展所提出的成像算法。所提出的方法在模拟和真实数据上进行了评估，并显示出对测量中射频干扰 (RFI) 存在的鲁棒性的显着改进。