Low-rank approximated Kalman-Bucy filters using Oja's principal component flow for linear time-invariant systems

Autor: Tsuzuki, Daiki, Ohki, Kentaro
Rok vydání: 2024
Předmět:
Zdroj: IEEE Control Systems Letters, Vol. 8, pp. 1583-1588, 2024
Druh dokumentu: Working Paper
DOI: 10.1109/LCSYS.2024.3412150
Popis: The Kalman-Bucy filter is extensively utilized across various applications. However, its computational complexity increases significantly in large-scale systems. To mitigate this challenge, a low-rank approximated Kalman--Bucy filter was proposed, comprising Oja's principal component flow and a low-dimensional Riccati differential equation. Previously, the estimation error was confirmed solely for linear time-invariant systems with a symmetric system matrix. This study extends the application by eliminating the constraint on the symmetricity of the system matrix and describes the equilibrium points of the Oja flow along with their stability for general matrices. In addition, the domain of attraction for a set of stable equilibrium points is estimated. Based on these findings, we demonstrate that the low-rank approximated Kalman--Bucy filter with a suitable rank maintains a bounded estimation error covariance matrix if the system is controllable and observable.
Comment: fixed a few typos
Databáze: arXiv