Artificial Intelligence-Aided Kalman Filters: AI-Augmented Designs for Kalman-Type Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976514" target="_blank" >RIV/49777513:23520/25:43976514 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/MSP.2025.3569395" target="_blank" >https://doi.org/10.1109/MSP.2025.3569395</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MSP.2025.3569395" target="_blank" >10.1109/MSP.2025.3569395</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Intelligence-Aided Kalman Filters: AI-Augmented Designs for Kalman-Type Algorithms
Original language description
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task oriented and SS model oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study (whose code is publicly available), illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
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
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Signal Processing Magazine
ISSN
1053-5888
e-ISSN
1558-0792
Volume of the periodical
42
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
25
Pages from-to
52-76
UT code for WoS article
001575790500001
EID of the result in the Scopus database
2-s2.0-105006853874