Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
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Astrophysics > Instrumentation and Methods for Astrophysics
Title:Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
Abstract: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.
| Subjects: | Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2606.23944 [astro-ph.IM] |
| (or arXiv:2606.23944v1 [astro-ph.IM] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23944
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mohammed Nabil El Korso M. N. El Korso [view email][v1] Mon, 22 Jun 2026 21:05:31 UTC (8,829 KB)
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