# Tag Info

1

The Kalman filter is one of those interesting algorithms which are completely impenetrable if you don't have the underlying math background (multivariate statisics, in this case), but become utterly obvious in in hindsight when you do. Every "why" question about the plain old Kalman filter can be answered by looking at its problem statement, which ...

2

First, you err in using the square root of accuracy in your covariance matrices. These should be squared. If your positional accuracy is $\sigma_h$ and your velocity accuracy is $\sigma_s$, then your measurement matrix should be \mathbf R = \begin{bmatrix} \sigma_h^2 & 0 & 0 & 0 \\ 0 & \sigma_h^2 & 0 & 0 \\ 0 & 0 & \...

0

Maybe what is extremely slow is python, if you are comparing with a rolling average function implemented in some library.Specially if the dimension of the observations large, each step it will invert a matrix, $O(n^3)$, it seems you have only one observation in your example. Even for similar implementations Kalman filter will be slower than moving average. ...

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