Solved – Kalman filter parameter estimation

From what I've known about Kalman filter, it requires all the parameters of the underlying state space model. Say the state space model is:
$$xi_{t+1} = Fxi_t + v_{t+1}$$
$$y_t = Hxi_t + w_{t}$$
where $v$ and $w$ are disturbances.
Kalman filter needs the $F$, $H$, $Q$ (the covariance matrix of $v$) and $R$ (the covariance matrix of $w$) as well as $xi_1$ as the initial state and the corresponding $P_1$ (the mean squared error of $xi_1$) to start the recursion.
However, these parameters generally have to be estimated by numerical methods. Assume $y_t$ is Gaussian, how can the $H$ be estimated if we don't have any external knowledge about $xi_t$? Alternatively, if we have some external knowledge of $H$, the estimation of all the parameters can be proceeded as well. What is the general rule to determine the initial parameter values or the state vector if we don't have any external knowledge?

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