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Dear HN: Please don't let this comment discourage you from reading the article.

Kalman filters are super useful tools across many domains.

They are a primary instrument in weather forecasting, robotics, finance, and more. Data you see every day have been touched by this technology.




Thanks for your contextualization. The above comment really did hurt my desire to learn more about Kalman filters. I know just being negative or contrary to a thing has an unreasonably high return on investment in making a commenter look smart or authoritative, but it sure does harm.


To add onto this, it’s used in multiple places in GPS and other positioning problems (as in I was on the CoreLocation team at Apple years ago and Kalman filters were common). I’m not really sure where the commenter is sourcing their claim but my experience directly contradicts it.


The article is very good and pedagogical.

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.


> just a convoluted notation for guesswork.

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.




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