Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

• URL: http://arxiv.org/abs/2606.23944v1
• Date: Mon, 22 Jun 2026 21:05:31 GMT
• Title: Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
• Abstract summary: We propose a robust estimation scheme for linear state--space models subject to compound-Gaussian noise.<n>We show that the proposed method significantly improves reconstruction fidelity and inference to radio-frequency interference (RFI)<n>These results highlight the benefits of heavy-tailed state--space modeling and SAEM-based inference in interference-dominated imaging scenarios.
• Score: 5.025813884984533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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