Unscented Kalman Filter - Multiple Consecutive Measurement Updates
I will use Wikipedia notations - Kalman Filter.
In most models the state transition model matrix $ F $ depends on the interval parameter $ T $. The same goes for the Process Noise Covarinace Matrix $ Q $. For instance, for the constant velocity model:
$$ F = \begin{bmatrix}
1 &amp; T \\ 
0 &amp; 1
\end{bmatrix}, \; Q = \begin{bmatrix}
\frac{ {T}^4 }{4} &amp; \frac{ {T}^3 }{2} \\ 
\frac{ {T}^3 }{2} &amp; {T}^{2}
\end{bmatrix} $$
Now, one simple policy you act by, whihc works perfectly in real life and is highly flexible, is as following:

Always remember the time stamp of the last Kalman step, be it prediction or update.
Given new input which is not a measurement do as following:

Take the input time stamp.
Define $ T = {t}_{current} - {t}_{last} $.
Build $ F $, $ Q $ and all other model matrices based on the given $ T $.
Apply prediction step.
Update the time stamp of the last Kalman step to $ {t}_{current} $.


Given new input which is a measurement do as following:

Take the input time stamp.
Define $ T = {t}_{current} - {t}_{last} $.
Build $ F $, $ Q $ and all other model matrices based on the given $ T $.
Apply prediction step.
Apply update step.
Update the time stamp of the last Kalman step to $ {t}_{current} $.



This works and it is very robust. Yet it has the overhead that you need to generate the correct matrices each step.
Also, since the UKF (Unscented Kalman filter) is different only in its Update step, it holds for it as well (For EKF as well).