  A Kalman filter can be used to determine material parameters using uncertain
experimental data. However, starting with inappropriate initial values for
material parameters might include false local attractors or even divergence.
Also, inappropriate choices of covariance errors of initial state, present
state, and 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 Kalman filter based on known parameters that are used to
generate the data with different noises used as 'measurement data'. The method
consists of two steps. First, an appropriate range of parameter values is
chosen based on a graphical representation of the mean square error. Second,
the Kalman filter is used based on the selected range and the suggested
parameters and errors. The method of the filter significantly reduces the
iteration time, and covers a wide range of initial suggested values for the
parameters compared with the standard Kalman filter. When the methodology is
applied to real data, very good results are obtained. Diffusion coefficient for
bovine bone is chosen to be a case study in this work.
