  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.
