Kalman filters are ideal for systems which are continuously changing. And it can take advantage of correlations between crazy phenomena that you maybe wouldn’t have thought to exploit! Even if messy reality comes along and interferes with the clean motion you guessed about, the Kalman filter will often do a very good job of figuring out what actually happened. You can use a Kalman filter in any place where you have uncertain information about some dynamic system, and you can make an educated guess about what the system is going to do next.
At times its ability to extract accurate information seems almost magical- and if it sounds like I’m talking this up too much, then take a look at this previously posted video where I demonstrate a Kalman filter figuring out the orientation of a free-floating body by looking at its velocity. Surprisingly few software engineers and scientists seem to know about it, and that makes me sad because it is such a general and powerful tool for combining information in the presence of uncertainty. I have to tell you about the Kalman filter, because what it does is pretty damn amazing.
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