Kalman Filter - Optimal Way to Handle "Derived" Measurements?
In ideal world you'd have the correct model and use it.
In your case, the model isn't perfect.
Yet the steps you're suggesting are based on a knowledge you have about the process - which you should incorporate into your process equation using your dynamic model matrix:

The classic and correct way given F matrix is built correctly according to your knowledge.

&quot;Extra&quot; prediction step will yield nothing, since $ {F}_{ik} = {F}_{ij} {F}_{jk} $ and if you reduce the time frame you should alter $ Q $ and $ R $ accordingly which should get you at the end of the chain of small steps the same $P_{k \mid k - 1}$.

If you don't measure V you'll must &quot;Estimate&quot; it somehow. Yet by definition, if your case falls under Kalman's assumptions using Kalman's filter would yield best results.


All in all, stick with the &quot;Classic&quot;.