Q matrix and updating times in a Kalman filter
Kalman filter works in a predictor-corrector or predict-update sequence. When no new measurement is available the only thing that you can do is to predict(NOT update) the state with your assumed dynamic model. The prediction is done not only on the state vector itself but also on its covariance matrix ${\bf{P}}$ (Not the measurement covariance matrix $\bf{R}$). A popular model commonly used for model regularization (AKA model noise matrix, or the matrix $\bf{Q}$) is the Singer model from 1990 (see [1]).

In your case the transition matrix is given by $\Phi_k =  \begin{bmatrix}1 &amp; dt \\0 &amp; 1 \end{bmatrix}$ , so the state covariance matrix is predicted using 
${\bf{P}}_{k|k-1}=\Phi_k {\bf{P}}_{k-1|k-1} \Phi_k^T + {\bf{Q}}_k$. That way the state matrix ${\bf{P}}$ "inflates" to account for the uncertainty.

[1] Singer, R. A., “Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-6, no. 4, .pp. 473-483, July 1990.