Continuous-time Kalman Filter (Dr. Jake Abbott, University of Utah)

[@cendit420]

Just watched the LQR before and now this. Great explanation!

[@LNasterio]

I came from an university ranks with single digit, and I believe you are more qualified as a teacher in my view than most of my professors. My professor couldn't give a shit about teaching.

[@pedroserrano7720]

This channel has some really good content!!

[@mashiur033]

Impressive I love the way he explained everything....

[@wolframjar]

Pedagogically a very nice presentation. In addition to the quick reference to the matrix Riccati equation at the end of the presentation, the full appreciation of the non-stationary Kalman filter, exceeding the possibilities of the Wiener filter, should be noted. In particular the measurement and driving noise sources V(t) and W(t) can themselves be time variable. See p. 288, Sage & Melsa, McGraw Hill, 1971.

[@dr.seaaral-dabooni383]

I really appreciate for your effort to explain and learn the linear control system. Thanks

[@ruke1ire]

watched all of your control videos:) Thank you very much, I really really appreciate the work.

[@chethingaherath9642]

thank you,can you upload kalman filter for discrete time please

[@Rikkysteiner]

Thank you, this video really helped me understand how to use the kalman filter

[@vashistnarayansingh5995]

Suppose i am using kalman on time series data and I want a 60 day window then putting T = 60 does my job or is there any other way for this ?

[@robertheal5137]

The noise disturbance d between x(t+1) and x(t) is not the same as the noise disturbance to the derivative x dot.

[@evilrho]

Well done. I used this to inspire one of my own lectures on this subject. Can you tell me what software/hardware you're using to make this video?

[@cephasatheos6627]

As a somewhat dysmathic person teaching themselves higher-order maths, I really enjoyed the explanations and description of the relationships between the various variables. It actually made kinda sense! But... The mistakes, for me, made it really distracting, because of my tenuous grasp. So I followed along, fat, dumb, and happy, then had to stop an rethink when the variable was incorrectly described as a state instead of a vector, tau instead of t, and so on. That's just me, I know, but still.

[@juderobise7201]

Big thanks!