<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper summarises and describes the most commonly used matrix factorisation methods applied in design of the Kalman filter in order to improve computational efficiency and avoid divergence issues caused by numerical round-off and truncation errors. Some forms of the Kalman filter are more prone to the growth of numerical error sand possible divergence than other implementations. In order to prevent the algorithm’s divergence additional processing is needed and this paper discusses pros and cons of different implementations and their numerical characteristics. Numerical issues still arise in finite word length implementations of algorithms, which frequently occur in embedded systems. This paper describes algorithms based on different factorisations such as Cholesky, U-D, SVD and their basic numerical properties.</p>
</abstract>

The paper summarises and describes the most commonly used matrix factorisation methods applied in design of the Kalman filter in order to improve computational efficiency and avoid divergence issues caused by numerical round-off and truncation errors. Some forms of the Kalman filter are more prone to the growth of numerical error sand possible divergence than other implementations. In order to prevent the algorithm’s divergence additional processing is needed and this paper discusses pros and cons of different implementations and their numerical characteristics. Numerical issues still arise in finite word length implementations of algorithms, which frequently occur in embedded systems. This paper describes algorithms based on different factorisations such as Cholesky, U-D, SVD and their basic numerical properties.

Overview of Factorisation Methods in Kalman Filtering

B&H Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Overview of Factorisation Methods in Kalman Filtering"}

The paper summarises and describes the most commonly used matrix factorisation methods applied in design of the Kalman filter in order to improve computational...

Overview of Factorisation Methods in Kalman Filtering

Pubblicato online: 01 nov 2022

Pagine: 51 - 60

Ricevuto: 01 mar 2020

Accettato: 01 giu 2020

DOI: https://doi.org/10.2478/bhee-2020-0006

Parole chiave
Kalman filter, matrix factorisation, orthogonal transformation, numerical methods

© 2020 Asim Vodenčarević, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Overview of Factorisation Methods in Kalman Filtering

Pubblicato online: 01 nov 2022

Pagine: 51 - 60

Ricevuto: 01 mar 2020

Accettato: 01 giu 2020

DOI: https://doi.org/10.2478/bhee-2020-0006

Parole chiaveKalman filter, matrix factorisation, orthogonal transformation, numerical methods

© 2020 Asim Vodenčarević, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Kalman filter, matrix factorisation, orthogonal transformation, numerical methods