# Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

arXiv: https://arxiv.org/abs/2606.23944 · HTML: https://mlsanity.com/paper/2606.23944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [Robust Particle Filter by Dynamic Averaging of Multiple Noise Models](https://mlsanity.com/paper/1609.01336/) (2016)
- [Robust and Trend Following Student's t Kalman Smoothers](https://mlsanity.com/paper/1303.5588/) (2013)
- [Robust Radio Interferometric Calibration Using the t-Distribution](https://mlsanity.com/paper/1307.5040/) (2013)
- [Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches](https://mlsanity.com/paper/2506.01696/) (2025)

## Abstract (arXiv)

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.