[em500]

If you have separate estimates / noisy measurements of the same quantity, and also have some estimate of their variance (e.g. pollsters or weather forecasters with different accuracy track records), you'd probably want a weighted mean (inversely by their variance) rather than a simple mean. If you have a system that runs for some time, you'd usually (but not always) want an average with higher weights placed on the recent past than on the distant past. (The arithmetic mean is the optimal estimate in a model where the true value is unknown but time-constant.)

The Kalman filter is "just" a recipe to calculate the weights for optimal estimation, given different model assumptions. The difficult part is not applying the formulas, but mostly in coming up with a good model of the movements of the things you want to measure/estimate.