State estimation in linear dynamical systems by partial update Kalman filtering

Pourasad, Y, Vahidpour, V, Rastegarnia, A, Ghorbanzadeh, P and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2021. State estimation in linear dynamical systems by partial update Kalman filtering. Circuits, Systems, and Signal Processing. ISSN 0278-081X

Preview

Text 1464378_Sanei.pdf - Post-print Download (2MB) | Preview

Abstract

In this letter, we develop a partial update Kalman filtering (PUKF) algorithm to solve the state of a discrete-time linear stochastic dynamical system. In the proposed algorithm, only a subset of the state vector is updated at every iteration, which reduces its computational complexity, compared to the original KF algorithm. The required conditions for the stability of the algorithm are discussed. A closed-form expression for steady-state mean-square deviation is also derived. Numerical examples are used to validate the correctness of the provided analysis. They also reveal the PUKF algorithm exhibits a trade-off between the estimation accuracy and the computational load which is extremely profitable in practical applications.

Item Type:	Journal article
Publication Title:	Circuits, Systems, and Signal Processing
Creators:	Pourasad, Y., Vahidpour, V., Rastegarnia, A., Ghorbanzadeh, P. and Sanei, S.
Publisher:	Springer Science and Business Media LLC
Date:	16 August 2021
ISSN:	0278-081X
Identifiers:	Number Type 10.1007/s00034-021-01815-5 DOI 1464378 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	31 Aug 2021 09:15
Last Modified:	16 Aug 2022 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/44091

Actions (login required)

Edit View

Statistics

Views

Downloads