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When learning the Kalman filter, it clicks in place much faster when there are two or more inputs with different noise profiles. That's why it exists and that's what was its original use-case.

Yet virtually all tutorials stick to single-input examples, which is really an edge case. This site is no exception.

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Kalman filters have always been about state estimation. What you consider an exception is the default in the vast majority of state estimation scenarios.

Before I got into control theory, I've read a lot of HN posts about kalman filters being the "sensor fusion" algorithm, which is the wrong mental model. You can do sensor fusion with state estimation, but you can't do state estimation with sensor fusion.

I have a chapter in my book that introduces sensor fusion as a concept. If you want to dive deeper into the sensor fusion topic, I would recommend Bar-Shalom's or Blackman's book.

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