Always telling this whenever the topic of Kalman Filters come up:
If you're learning the Kalman Filter in isolation, you're kind of learning it backwards and missing out on huge "aha" moments that the surrounding theory can unlock.
To truly understand the Kalman Filter, you need to study Least Squares (aka linear regression), then recursive Least Squares, then the Information Filter (which is a different formulation of the KF).
Then you'll realize the KF is just recursive Least Squares reformulated in a way to prioritize efficiency in the update step.
If you're learning the Kalman Filter in isolation, you're kind of learning it backwards and missing out on huge "aha" moments that the surrounding theory can unlock.
To truly understand the Kalman Filter, you need to study Least Squares (aka linear regression), then recursive Least Squares, then the Information Filter (which is a different formulation of the KF). Then you'll realize the KF is just recursive Least Squares reformulated in a way to prioritize efficiency in the update step.
This PDF gives a concise overview:
[1] http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/...