These are chat archives for **rlabbe/Kalman-and-Bayesian-Filters-in-Python**

I intend to add a section on just this topic in a few day. In the meantime, I suggest looking up 'mahalanobis distance', which is a measure of how far a measurement is from the KF's prior. You can use this to 'gate' your data - discard data that is "too far away". Theory says throw away anything > 3 std, but in practice you may find 4,5, even 6 std to be a better gating distance

If you throw the data away, you just don't call update for that time period. You will thus call predict twice in a row, and your estimate will gain uncertainty because you did 2 predictions in a row

that's the general idea. The search term "kalman filter gating" is also a fruitful search.