arXiv:2606.23944v1 Announce Type: cross 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 sa
The increasing complexity and volume of data from radio interferometry, especially with challenges like Radio-Frequency Interference (RFI), necessitate more robust and efficient signal processing techniques.
Improved robust estimation for state-space models in fields like radio interferometry indicates a broader trend toward more resilient and accurate AI/ML applications in scientific and industrial domains.
The ability to more accurately analyze dynamical systems with heavy-tailed or outlier-prone noise, using methods like Stochastic Approximation Expectation-Maximization (SAEM), enhances data integrity for AI models deployed in harsh or noisy environments.
- · Radio astronomy researchers
- · AI/ML developers in industrial applications
- · Satellite communication companies
- · Defence technology developers
- · Legacy signal processing techniques
- · Systems heavily reliant on Gaussian noise assumptions
More precise and reliable data analysis in radio interferometry leads to clearer insights into cosmic phenomena.
The robust estimation scheme could be adapted to other fields facing similar noise challenges, expanding its applicability beyond astronomy.
Enhanced data quality and resilience could accelerate the development of autonomous systems operating in signal-congested or adverse environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG