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.
Make a robot friend with Adafruit’s CRICKIT – A Creative Robotics & Interactive Construction Kit. It’s an add-on to our popular Circuit Playground Express, FEATHER and other platforms to make and program robots with CircuitPython, MakeCode, and Arduino. Start controlling motors, servos, solenoids. You also get signal pins, capacitive touch sensors, a NeoPixel driver and amplified speaker output. It complements & extends your boards so you can still use all the goodies on the microcontroller, now you have a robotics playground as well.