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I don't know what it is about the Kalman filter but so many explanations including the OP have this format: "It's very simple! <complicated obtuse explanation
by foobarian 3y ago
I don't know what it is about the Kalman filter but so many explanations including the OP have this format: "It's very simple! <complicated obtuse explanation listing the computation steps>"
Your comment is the first I've seen actually providing intuition about what is happening. It doesn't help perhaps that the name itself is misleading as heck to computer people like me: it's not a filter as in stream processing or SQL.
- shiandow 3y agoI recall reading a very intuitive explanation including animations of a point cloud to show how it works. I've had no luck finding the article again though.
- jvanderbot 3y agoThe problem is 'simple' can mean a few things: - simple to predict / understand what it would do: Easily explained with pictures and hardly no math - simple to understand at a higher level / see why it works: Not easily explained without math and fraught with bayesian vs optimization vs EE-type approaches - simple to understand well enough to use: Not easily done without other relevant math that is not covered in KF explanation, e.g., controls, matrix analysis, Jacobians, etc - simple to understand why the equations are named what they are, and why they work: Not easily explained without math and historical context that takes a page or so to explain. > it's not a filter as in stream processing As you say "filter" means 'remove noise', but it also means 'process in order of arrival', so it's similar to your def of filter. So, we really need 4 guides.
- dwrtz 3y agoyeah the "filter" term means something different in the KF context and is confusing filter: use measurements up to time t to estimate the state at time t smooth: use past and future measurements to estimate the state at time t predict: use measurements up to time t to estimate the state at time t+n