Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976518" target="_blank" >RIV/49777513:23520/25:43976518 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TSP.2025.3581703" target="_blank" >https://doi.org/10.1109/TSP.2025.3581703</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2025.3581703" target="_blank" >10.1109/TSP.2025.3581703</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
Original language description
Recent years have witnessed a growing interest in tracking algorithms that augment Kalman filters (KFs) with deep neural networks (DNNs). By transforming KFs into trainable deep learning models, one can learn from data to reliably track a latent state in complex and partially known dynamics. However, unlike classic KFs, conventional DNN-based systems do not naturally provide an uncertainty measure, such as error covariance, alongside their estimates, which is crucial in various applications that rely on KF-type tracking. This work bridges this gap by studying error covariance extraction in DNN-aided KFs. We begin by characterizing how uncertainty can be extracted from existing DNN-aided algorithms and distinguishing between approaches by their ability to associate internal features with meaningful KF quantities, such as the Kalman gain and prior covariance. We then identify that uncertainty extraction from existing architectures necessitates additional domain knowledge not required for state estimation. Based on this insight, we propose Bayesian KalmanNet, a novel DNN-aided KF that integrates Bayesian deep learning techniques with the recently proposed KalmanNet and transforms the KF into a stochastic machine learning architecture. This architecture employs sampling techniques to predict error covariance reliably without requiring additional domain knowledge, while retaining KalmanNet's ability to accurately track in partially known dynamics. Our numerical study demonstrates that Bayesian KalmanNet provides accurate and reliable tracking in various scenarios representing partially known dynamic systems.
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 Transactions on Signal Processing
ISSN
1053-587X
e-ISSN
1941-0476
Volume of the periodical
73
Issue of the periodical within the volume
June 2025
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
2558-2573
UT code for WoS article
001525462300004
EID of the result in the Scopus database
2-s2.0-105009290864