Pardon my ignorance, I'm just wondering about some context, since the Kalman filter was invented in the 60s. Are Kalman filters still highly relevant, or are they (in practice and/or in theory) obsoleted by other techniques, such as general ML?
Kalman filters are never going to completely go away since they are optimal[1] under certain assumptions. But these assumptions are often not met so you have to start approximating (with EKF or UKF). In those scenarios, once computational power allows, I think other approaches such as particle filters which can handle arbitrary distributions(e.g., multimodal) will start taking over. But we're not there yet(?). [1] ht…
I agree both EKF and UKF are what you'll find in real world applications (because computing Jacobians is a pain!). I find the work of Jeffery Uhlmann both incredibly useful and hilarious (in that the "Unscented Kalman Filter" came from a stick of deodorant on a co-workers desk).