“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.”
A Kalman filter takes a guess, takes a noisy measurement and blends the two by how much it trusts each. It keeps no history beyond the last state, so it runs on tiny chips in real time. Most explanations online bury it under scary, opaque math. A few Gaussian blobs explain it better.