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This article is fantastic.

For those who have some familiarity with Python, I found this to be a great resource for Kalman Filtering: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...



Seconded! I worked through this partway and appreciate some of the intuitions they build up.

It seems to me that there is no shortcuts to grokking the KF than going through simpler filters/Bayesian update algorithms.

Another resource I personally enjoy is Student Dave's lectures on YouTube. He's really entertaining and shows both pen-and-paper examples and Matlab simulations.


Excellent, thank you! The article did a good job telling me what a Kalman filter looks like in math, but I really wanted one that told me what it looks like in code.




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