A methodology for using Kalman filter to determine material parameters from uncertain measurements

A Kalman filter can be used to determine material parameters using uncertain experimental data. However, starting with inappropriate initial values for material parameters might cause false local attractors or even divergence. Also, inappropriate choices of covariance errors of the state and the measurements might affect the stability of the prediction. The present method suggests a simple way to predict the parameters and the errors required to start the Kalman filter based on known parameters and generated data with different noises used as “measurement data”. Diffusion coefficients for bovine bone and viscoplastic steel parameters are chosen as case studies in this work.