Identifying trending coefficients with an ensemble Kalman filter

TL;DR

This paper enhances the ensemble Kalman filter to dynamically identify trending model coefficients, demonstrating improved robustness and accuracy in tracking changes over time compared to classic methods.

Contribution

It introduces an inflation technique for the ensemble Kalman filter to better track time-varying coefficients in inverse problems.

Findings

Effective dynamic coefficient identification demonstrated.

Inflated EnKF shows robustness to initial conditions.

Good static accuracy achieved.

Abstract

This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a classic EnKF and a recursive least squares (RLS) on the example of identifying a force model in milling, which changes due to the progression of tool wear. For a proper comparison, the true values are simulated and augmented with white Gaussian noise. The results demonstrate the feasibility of the approach for dynamic identification while still achieving good accuracy in the static case. Further, the inflated EnKF shows a remarkably insensitivity on the starting set but a less smooth convergence compared to the classic EnKF.