Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
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Computer Science > Machine Learning
Title:Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
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.
| Comments: | Accepted by TMLR |
| Subjects: | Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2606.14195 [cs.LG] |
| (or arXiv:2606.14195v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14195
arXiv-issued DOI via DataCite (pending registration)
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