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.
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Biohacking — Vitamin-C + Gelatin for Accelerated Recovery
Python for Microcontrollers — The Top Programming Languages 2019 – Python tops the charts with a CircuitPython nod! #Python #Adafruit #CircuitPython #PythonHardware @circuitpython @micropython @ThePSF @Adafruit