[Paedor]

Conceptually, every step of applying a Kalman filter in the simplest case is just updating a Gaussian prior representing position with a new datapoint sampled with (assumed) Gaussian error. The result is equivalent to convolving a Gaussian representing position with a Gaussian representing noise.

In the frequency domain, that's equivalent to multiplying one Gaussian by another one with zero mean, which will always put higher weight on low frequencies. No matter what the gain is in the Kalman filter, that'll still be true as far as I can tell. As the gain varies, the cutoff within the low-pass filter will change though.

It gets harder to analyze when you start using non-linear models to update position though. Generally, I think the same logic applies.