Kalman Filter via a Simple and Intuitive Derivation [pdf]
Maybe it's just because I'm not the target audience for this paper, but I'm finding this very tough going. I'm a PhD student in a mathematical field (operations research) but have only the faintest idea about Kalman filters - something about updating beliefs based on noisy measurements in a way that feels intuitively similar to Bayes' Rule. I'm not a tutor intending to teach Kalman filters to anyone in the near future, fair, but I should have the mathematical background to get through this. Not sure why it feels like a slog. Maybe it's because the measurement and prediction and update equations appear before any intuition about what a Kalman filter is, what the stages of the algorithm are, etc.?Edit: all the way through now. Certainly this would be much more useful as an intro to Kalman filters (rather than an introduction to introducing Kalman filters) had some intuition been given.