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A Kalman filter can be used to determine material parameters using uncertainexperimental data. However, starting with inappropriate initial values formaterial parameters might include false local attractors or even divergence.Also, inappropriate choices of covariance errors of initial state, presentstate, and measurements might affect the stability of the prediction. Thepresent method suggests a simple way to predict the parameters and the errors,required to start Kalman filter based on known parameters that are used togenerate the data with different noises used as 'measurement data'. The methodconsists of two steps. First, an appropriate range of parameter values ischosen based on a graphical representation of the mean square error. Second,the Kalman filter is used based on the selected range and the suggestedparameters and errors. The method of the filter significantly reduces theiteration time, and covers a wide range of initial suggested values for theparameters compared with the standard Kalman filter. When the methodology isapplied to real data, very good results are obtained. Diffusion coefficient forbovine bone is chosen to be a case study in this work.

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