In this study, we developed a method of Ensemble Kalman Filter(EnKF)-based data assimilation to estimate parameters in dislocation dynamics(DD) simulations. DD simulations have been widely used in studies on crystal plasticity. However, it is challenging to obtain the parameters necessary to reproduce the dislocations observed in experiments using DD simulations. Data assimilation is a method for statistically correcting the uncertainties in numerical simulations using observational data. Data assimilation is widely used in many fields for model parameter estimation and state estimation. We aim to obtain the parametes in DD simulations such as mobilities by using the data assimilation method. We performed twin experiments to validate the data assimilation method based on EnKF in DD simulations. We used ParaDiS as the DD simulations and generated synthetic observation data with pre-assumed true parameter values. Then, we estimated the true parameter value using DD simulations with a parameter different from the true value. Through the twin experiments, we demonstrated that the method in this study successfully estimated the mobility of dislocations. As the next step, we will apply the proposed method to data assimilation between transmission electron microscopy videos and DD simulations.