My take on Kalman filter is that they are, with a diagonal regression matrix and precomputed parameters, just a convoluted notation for guesswork. It is abit like drawing Nyquist diagrams for system stability analysis - mostly an academic excercise. And stuff like that plagues control theory. I would rather that students learned to keep it simple.
Kalman filters are literally everywhere in the industry. If there is a radar or data fusion involved, you can be pretty sure there are Kalman filters. I know a researcher whose most quoted article is just him applying fancy new methods to actual industrial datasets and showing they perform worse than a Kalman filter.
What you wrote is akin to someone explaining to students doing signal processing that they should stay away from Fourier transforms.
You could say that for all of estimation, by definition. But some estimates are better than others, and the KF is the best estimate under certain conditions...
...and one of those conditions is that you have a good estimate of the dynamics and measurement noise parameters. Rather than throw our hands up, we should just articulate this, and proceed to discuss methods for getting a good estimate of noise parameters, and discuss what happens if our estimates are wrong.
My take on Kalman filter is that they are, with a diagonal regression matrix and precomputed parameters, just a convoluted notation for guesswork. It is abit like drawing Nyquist diagrams for system stability analysis - mostly an academic excercise. And stuff like that plagues control theory. I would rather that students learned to keep it simple.