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Maybe check out Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard. It has a coherent Bayesian formulation with consistent notation on many Kalman-related topics. Also with the rise of AI/ML, classic control theory ideas are being merged with reinforcement learning.

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I agree that Bayesian filtering is the most general and logical approach. There are Bayesian derivations of the Kalman filter too.

Here is a broad survey: https://people.bordeaux.inria.fr/pierre.delmoral/chen_bayesi...

Thanks for the recommendation! It would never have occurred to me to look at robotics, but I can understand why that's very relevant.

I read Feedback Control for Computer Systems not too long ago, which felt like yet another restatement of the same ideas; I guess that counts as "classic control theory".

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