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Bibliographic Details
Main Authors: Tsuzuki, Daiki, Ohki, Kentaro
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2407.05675
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author Tsuzuki, Daiki
Ohki, Kentaro
author_facet Tsuzuki, Daiki
Ohki, Kentaro
contents The Kalman filter is indispensable for state estimation across diverse fields but faces computational challenges with higher dimensions. Approaches such as Riccati equation approximations aim to alleviate this complexity, yet ensuring properties like bounded errors remains challenging. Yamada and Ohki introduced low-rank Kalman-Bucy filters for continuous-time systems, ensuring bounded errors. This paper proposes a discrete-time counterpart of the low-rank filter and shows its system theoretic properties and conditions for bounded mean square error estimation. Numerical simulations show the effectiveness of the proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2407_05675
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Low-rank approximated Kalman filter using Oja's principal component flow for discrete-time linear systems
Tsuzuki, Daiki
Ohki, Kentaro
Optimization and Control
Systems and Control
The Kalman filter is indispensable for state estimation across diverse fields but faces computational challenges with higher dimensions. Approaches such as Riccati equation approximations aim to alleviate this complexity, yet ensuring properties like bounded errors remains challenging. Yamada and Ohki introduced low-rank Kalman-Bucy filters for continuous-time systems, ensuring bounded errors. This paper proposes a discrete-time counterpart of the low-rank filter and shows its system theoretic properties and conditions for bounded mean square error estimation. Numerical simulations show the effectiveness of the proposed method.
title Low-rank approximated Kalman filter using Oja's principal component flow for discrete-time linear systems
topic Optimization and Control
Systems and Control
url https://arxiv.org/abs/2407.05675