aiqbench analysis
This research proposes a novel Bayesian filtering framework using Embedded Latent Transfer Operators (ELTO) to overcome the limitations of static noise models in non-stationary environments. By coupling data-driven learning with dynamic parameter adaptation, the method significantly improves state estimation accuracy for time-varying processes without relying on simplified assumptions. The work advances sequential machine learning techniques specifically designed for complex, evolving dynamical systems where traditional Kalman filters struggle.
Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
arXiv:2606.14195v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2606.14195 [cs.LG]
(or arXiv:2606.14195v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14195 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 207 words
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