Yes, because unlike an simple average you have a model for both what you are trying to measure and the measurement. For example, one basic model for your examp

Yes, because unlike an simple average you have a model for both what you are trying to measure and the measurement. For example, one basic model for your example could be if you are trying to measure a constant with some initial uncertainty, say it's Gaussian with a standard deviation, and noisy measurements with say also an uncertainty with Gaussian distribution and some standard deviation. You can tune the initial uncertainty (a number) around the constant you are trying to estimate , and the uncertainty in the measurements (another number than may or may not change). In this example a Kalman filter won't behave as an average. If the measurements are good (low uncertainty) they will converge quickly, if they are bad the estimate will jump around and take longer to converge. Anyway, I made a mess, I'm not good at explaining... And by the way, it's not true what people are suggesting here that Kalman filters are for moving things. They are used to estimate constants _all the time_ but yes, they're more popular for moving things.