Combining Kalman Filters
I know of no "standard" way of incorporating the output of a set of Kalman filters into another. The reason is that each application will have a different "signal model" (way of combining the multiple outputs into different measurements).

For example, suppose you have two Kalman filters that give you state estimates:
$$
\mathbf{x}_k^1 = \mbox{state of first Kalman filter}\\
\mathbf{x}_k^2 = \mbox{state of second Kalman filter}\\
$$
then you'll need to decide the parameters in your new model:
$$
\mathbf{x}^{\tt tot}_k = [ {\mathbf{x}^1_k}^T {\mathbf{x}^2_k}^T ]^T\\
\mathbf{x}^{\tt tot}_{k+1} = \mathbf{A} \mathbf{x}^{\tt tot}_k + v_k\\
\mathbf{z}^{\tt tot}_k = \mathbf{C} \mathbf{x}^{\tt tot}_k + w_k
$$
where $\mathbf{A}$ is the state update matrix, and $\mathbf{C}$ is the output matrix, $v_k$ is the process noise driving the interactions between the different states and $w_k$ is the measurement noise.