Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from their use of simplified noise models, which fail to dynamically adapt to non-stationary processes. To address this limitation, we introduce an ELTO-based Bayesian filtering approach with a new structured parameterization for the filter's noise model. This parameterization enables structured noise adaptation, which couples the data-driven learning of an optimal time-invariant noise model with dynamic parameter adaptation that responds to changes in dynamics within non-stationary processes. Empirical results show t...
| Authors | Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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