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-invar
The increasing complexity and non-stationarity of real-world data streams necessitate more robust and adaptive Bayesian filtering techniques for state estimation.
Improved sequential Bayesian filtering with adaptive noise models can significantly enhance the precision and reliability of AI systems operating in dynamic environments, from robotics to autonomous vehicles.
This research moves beyond simplified noise models, allowing AI systems to better handle unpredictable data and non-stationary processes, leading to more resilient and accurate estimations.
- · AI/ML researchers
- · Robotics industry
- · Autonomous systems developers
- · Real-time analytics platforms
- · Systems relying on static Kalman filters
- · Applications with high-consequence estimation errors
More accurate and robust AI models for sequential data processing and state estimation.
Accelerated development and deployment of autonomous systems in complex, unpredictable environments.
Enhanced AI capability to handle dynamic real-world scenarios, potentially reducing human intervention in critical operations.
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Read at arXiv cs.LG