Numerically robust Gaussian state estimation with singular observation noise
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916651799150592 |
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| author | Krämer, Nicholas Tronarp, Filip |
| author_facet | Krämer, Nicholas Tronarp, Filip |
| contents | This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule, transforming the singular estimation problem into a nonsingular one with reduced state dimension. In addition to ensuring low runtime and numerical stability, our proposal facilitates marginal-likelihood computations and Gauss-Markov representations of the posterior process. We analyse the proposed method's computational savings and numerical robustness and validate our findings in a series of simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Numerically robust Gaussian state estimation with singular observation noise Krämer, Nicholas Tronarp, Filip Methodology Machine Learning Numerical Analysis Computation This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule, transforming the singular estimation problem into a nonsingular one with reduced state dimension. In addition to ensuring low runtime and numerical stability, our proposal facilitates marginal-likelihood computations and Gauss-Markov representations of the posterior process. We analyse the proposed method's computational savings and numerical robustness and validate our findings in a series of simulations. |
| title | Numerically robust Gaussian state estimation with singular observation noise |
| topic | Methodology Machine Learning Numerical Analysis Computation |
| url | https://arxiv.org/abs/2503.10279 |