Koopman Kalman Filter (KKF): An asymptotically optimal nonlinear filtering algorithm with error bounds and its application to parameter estimation

TL;DR

This paper introduces the Koopman Kalman Filter (KKF), a novel nonlinear filtering algorithm leveraging Koopman operator theory and EDMD, providing error bounds, computational efficiency, and applications to parameter estimation.

Contribution

The paper presents KKF, an innovative nonlinear filtering method with theoretical error bounds, reduced computational complexity, and demonstrated effectiveness in both filtering and parameter estimation tasks.

Findings

KKF achieves error bounds of order O(N^{-1/2})

KKF has lower execution time compared to traditional methods

KKF performs comparably to MCMC in parameter estimation

Abstract

In this article, we propose a new filtering algorithm based in the Koopman operator, showing that a nonlinear filtering problem can be seen as an equivalent problem where the dynamics is infinite dimensional, but linear. Using Extended Dynamic Mode Decomposition (EDMD), we create a finite dimensional approximation of the filtering problem of dimension $N$, in state and error covariance matrix, that accomplishes an error bound of order \(O(N^{-1/2})\) in both where $N$ denotes the number of points used in the Koopman approximation. The algorithm is denominated Koopman Kalman Filter (KKF), and has computational complexity \(O(T\cdot N^3)\) in time, and \(O(T \cdot N^2)\) in space, where \(T\) is the number of iterations of the filtering problem. We test the algorithm in linear and nonlinear dynamics cases, showing and effective error bound with respect to the Kalman filter, that corresponds to the optimal solution in the linear case, and equals the error performance of other methods in the state of the art, but with a much lower execution time. Also, we propose a parameter estimation algorithm based in KKF, comparing it with Markov Chain Monte Carlo techniques, showing similar performance with lower execution time.