Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
Addresses the limitation of simplified noise models in ELTO-based filters for sequential state estimation in non-stationary processes.
The paper introduces a structured noise adaptation method for ELTO-based Bayesian filters, enabling dynamic noise model adaptation in non-stationary processes. Empirical results show improved state estimation in noisy, time-varying environments.
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 that our structured noise adaptation improves the filter's dynamic state estimation performance in noisy, time-varying environments.