From Sushi to Space: Particle Filters, Recursive Estimation, Covariance, and Kalman Filters Explained
I’m warning you now: You’ll need some time to read the article ‘How Kalman Filters Work, Part 1,’ and even then you might not understand it all. Nevermind that this is only part 1 (of 3) on LQE algorithms. As for the sushi reference, it’ll click as soon as you begin reading the article – and now I know what I want for lunch!
Interestingly, the most intuitive forms of recursive estimation are only recently becoming popular, so we’re going to look at their history entirely backwards: starting from the most recent types, like particle filters, and working back into the ancient past (the 1960s) for the breakthrough that enabled the Apollo navigation algorithms to keep a spacecraft on a course to the moon: the Kalman filter.
Adafruit has had paid day off for voting for our team for years, if you need help getting that going for your organization, let us know – we can share how and why we did this as well as the good results. Here are some resources for voting by mail, voting in person, and some NY resources for our NY based teams as well. If there are additional resources to add, please let us know – adafruit.com/vote
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