[quietbritishjim]

Agreed - a Kalman filter is often just a EMA in disguise.

I remember using a Kalman filter many years ago and getting confused when a measurement was fairly far from the current estimate, and the uncertainty still went down. Surely the uncertainty should go up if a measurement shows that our previous estimate was unreliable? I spent a lot of time debugging my code before someone pointed out to me that the Kalman filter does not adjust its uncertainties (and therefore the effective value of alpha) at all based on the data.

But a Kalman filter does not converge to EMA if either:

* Measurements occur at irregular intervals

* Measurements can have different errors (which you can meaningfully estimate)

These are exactly the situations when it's worth using a Kalman filter instead.