The Kalman filter is the most powerful tool for estimation of the states of a linear Gaussian system. In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model. However, this algorithm cannot function in real time. Thus, we propose a new method that can be used to estimate the transition matrices and the states of the system in real time. The proposed method uses three ideas: estimation in an observation space, a time-invariant interval, and an online learning framework. Applied to damped oscillation model, we have obtained extraordinary performance to estimate the matrices. In addition, by introducing localization and spatial uniformity to the proposed method, we have demonstrated that noise can be reduced in high-dimensional spatio-temporal data. Moreover, the proposed method has potential for use in areas such as weather forecasting and vector field analysis.
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