Dynamic Association of Semantics and Parameter Estimates by Filtering
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909989914804224 |
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| author | Greiff, Marcus Zhang, Ray Lew, Thomas Subosits, John |
| author_facet | Greiff, Marcus Zhang, Ray Lew, Thomas Subosits, John |
| contents | We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We extend existing methods to a multi-parameter setting using a posterior that tightly couples semantics with the parameter likelihoods, and propose a filter to compute this posterior sequentially, subject to dynamics in the map's state. Using Bayesian moment matching, we show that the computational complexity of measurement updates scales linearly in the dimension of the parameter space. Finally, we demonstrate limitations of applying existing methods to a problem from the driving domain, and show that the proposed framework better captures time-varying parameter-to-semantic associations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09158 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Association of Semantics and Parameter Estimates by Filtering Greiff, Marcus Zhang, Ray Lew, Thomas Subosits, John Systems and Control We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We extend existing methods to a multi-parameter setting using a posterior that tightly couples semantics with the parameter likelihoods, and propose a filter to compute this posterior sequentially, subject to dynamics in the map's state. Using Bayesian moment matching, we show that the computational complexity of measurement updates scales linearly in the dimension of the parameter space. Finally, we demonstrate limitations of applying existing methods to a problem from the driving domain, and show that the proposed framework better captures time-varying parameter-to-semantic associations. |
| title | Dynamic Association of Semantics and Parameter Estimates by Filtering |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.09158 |