Publication detail
Kalman Filter and Identifiability of the Observation Model
BENKO, M. ŽÁK, L.
English title
Kalman Filter and Identifiability of the Observation Model
Type
Paper in proceedings outside WoS and Scopus
Language
en
Original abstract
Kalman filter has become a commonly used approach in many technological applications to filter uncertainty from noisy measurements. In this paper, we describe the Kalman filter as an optimal unbiased estimator of the hidden state in the linear model. Additionally, a discussion of the model estimation is provided. In the end, as an original contribution, a discussion of the observation model is done.
Keywords in English
Hidden Markov model, Kalman filtering, Bayesian estimation, Parameter identifiability, Observation model
Released
2024-10-24
Publisher
Statistical Society of Slovenia
Location
Ljubljana, Slovenia
ISBN
978-961-94283-6-8
Book
23rd European Young Statisticians Meeting – Short of papers
Pages from–to
87–92
Pages count
6
BIBTEX
@inproceedings{BUT200985,
author="Matej {Benko} and Libor {Žák}",
title="Kalman Filter and Identifiability of the Observation Model",
booktitle="23rd European Young Statisticians Meeting – Short of papers",
year="2024",
pages="87--92",
publisher="Statistical Society of Slovenia",
address="Ljubljana, Slovenia",
isbn="978-961-94283-6-8",
url="https://www.researchgate.net/profile/Andrej-Srakar/publication/397412266_23rd_European_Young_Statisticians_Meeting_11-15_September_2023_Ljubljana_Slovenia_Proceedings/links/690f2cf1a404d65709a419ee/23rd-European-Young-Statisticians-Meeting-11-15-September-2023-Ljubljana-Slovenia-Proceedings.pdf#page=101"
}