I agree that Kalman filters are not magic and that having a reasonable model is essential for good performance.

Higher sampling rates can help in some cases, especially when tracking fast dynamics or reducing measurement noise through repeated updates. However, the main strength of the Kalman filter is combining a model with noisy measurements, not necessarily relying on high sampling rates.

In practice, Kalman filters can work well even with relatively low-rate measurements, as long as the model captures the system dynamics reasonably well.

I also agree that it's often something you design into the system rather than applying as a post-processing step.