Dynamic mode decomposition using a Kalman filter for parameter estimation

7Citations
Citations of this article
29Readers
Mendeley users who have this article in their library.

Abstract

A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combination with truncated proper orthogonal decomposition for many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can estimate eigenmodes more precisely compared with standard DMD or total least-squares DMD (tlsDMD) methods for the severe noise condition if the nature of the observation noise is known, though tlsDMD works better than KFDMD in the low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely even when the system matrix varies with time similar to online DMD, and this extension is naturally conducted owing to the characteristics of the Kalman filter. In summary, the KFDMD is a promising tool with strong antinoise characteristics for analyzing sequential datasets.

Cite

CITATION STYLE

APA

Nonomura, T., Shibata, H., & Takaki, R. (2018). Dynamic mode decomposition using a Kalman filter for parameter estimation. AIP Advances, 8(10). https://doi.org/10.1063/1.5031816

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free