Interpretable Augmented Physics-Based Model for Estimation and Tracking

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976508" target="_blank" >RIV/49777513:23520/25:43976508 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION65864.2025.11124036" target="_blank" >https://doi.org/10.23919/FUSION65864.2025.11124036</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION65864.2025.11124036" target="_blank" >10.23919/FUSION65864.2025.11124036</a>

Result language
angličtina
Original language name
Interpretable Augmented Physics-Based Model for Estimation and Tracking
Original language description
State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an accurate dynamical model for complex scenarios or adapting to changes in the system poses challenges to the estimation process. Recently, augmented physics-based models (APBMs) appear as an appealing strategy to cope with these challenges where the composition of a small and adaptive neural network with known physics-based models (PBM) is learned on the fly following an augmented state-space estimation approach. A major issue when introducing data-driven components in such a scenario is the danger of compromising the meaning (or interpretability) of estimated states. In this work, we propose a novel constrained estimation strategy that constrains the APBM dynamics close to the PBM. The novel state-space constrained approach leads to more flexible ways to impose constraints than the traditional APBM approach. Our experiments with a radar-tracking scenario demonstrate different aspects of the proposed approach and the trade-offs inherent in the imposed constraints.
Czech name
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Czech description
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Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2025 28th International Conference on Information Fusion (FUSION)
ISBN
978-1-03-705623-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Rio de Janiero, Brazílie
Event location
Rio de Janiero, Brazílie
Event date
Jul 7, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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