arch-dawson

5 ms·

1. 1.▲
   by arch-dawson 3y ago

   I agree. The author of this article lists some minor quibbles and then just says, "I'm sure there are more serious errors if someone looked properly."
2. 2.▲
   by arch-dawson 3y ago

   Kalman Filters track both an estimate x_hat and the variance of that estimate, P. There is also some true state, x_true. The definition of the variance of the estimate is E[(x_true - x_hat)^2], where E[.] is the expected value operator. Usi
3. 3.▲
   by arch-dawson 3y ago

   I enjoyed reading your article, but I want to note that the implementation is incorrect. The biggest error is that you are not propagating uncertainty forward in time, and therefore are underestimating the error. This shows up in your plots
4. 4.▲
   by arch-dawson 4y ago

   I just started a blog! I'm new to this kind of thing and so my first entry is just a short post on my preferred method for plotting covariance ellipses and ellipsoids.