Kalman Filtering with measurement latency
No, this sounds correct.  In a sense, you're using a model
$$\begin{align}x_k = F_{k-1} x_{k-1} + G_{k-1} u_{k-1}\\
y_k = H_k x_k\end{align}$$
But you're using $$H_k = \begin{cases}H_0 &amp; k \mod 10 = 0 \\ 0 &amp; \mathrm{otherwise}\end{cases}$$
and you're being mindful of the fact that your &quot;$x_k$&quot; is up to ten samples in the past -- so you need to predict $x_{k+1}$, $x_{k+2}$, etc.