But I agree, if a one-pole filter will solve your problem. Kalman filter is an optimal state estimator, useful when you have:
- multiple input and/or multiple output system
- non-trivial dynamics (i.e. the system of interest is in motion and you have a state space description of the dynamics)
- noisy measurements
- statistical estimates of process and measurement noise are available
- ideally, iid gaussian noises
Also good if you want to fuse data on different time scales or with different noise properties.
Easier to code, easier to tune parameters without a noise model