用随机期望最大化算法提升射电干涉成像抗干扰能力
Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
- 用蒙特卡洛采样替代标准E步，实现重尾噪声下的状态推断
- 在射电干扰环境下重建精度显著优于高斯模型和理想平滑器
- 适合射电天文等存在强干扰的动态系统成像任务
状态空间模型为动态系统分析提供了灵活框架，但常依赖高斯假设，难以处理重尾或含异常值的测量噪声。本文针对射电干涉中常见的复合高斯噪声（如射频干扰导致），提出一种鲁棒估计方法。该方法基于随机近似期望最大化（SAEM）算法，将标准E步替换为通过闭式吉布斯更新对隐状态与噪声纹理进行蒙特卡洛采样，从而在重尾似然下实现可计算推断。数值实验表明，该方法显著提升了重建保真度与抗射频干扰能力，优于高斯EM算法，甚至超越理想后向平滑器（oracle RTS smoother）。结果验证了重尾状态空间建模与基于SAEM的推断在干扰主导成像场景中的优势。
原文摘要 · Abstract (English)
State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture heavy-tailed or outlier-prone measurement noise. We propose a robust estimation scheme for linear state--space models subject to compound-Gaussian noise, as encountered for instance in radio interferometry affected by radio-frequency interference (RFI). The method relies on a Stochastic Approximation Expectation--Maximization (SAEM) algorithm in which the standard E-step is replaced by Monte Carlo sampling of the latent states and noise texture through closed-form Gibbs updates, enabling tractable inference despite the heavy-tailed likelihood. Numerical experiments show that the proposed method significantly improves reconstruction fidelity and robustness to RFI, outperforming a Gaussian EM algorithm and even an oracle RTS smoother. These results highlight the benefits of heavy-tailed state--space modeling and SAEM-based inference in interference-dominated imaging scenarios.
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