Comments (1)
Please consider the x as given in line 12 as
* x = Ax - KCx + Bu + Ky
the
-KCx
term should be in terms of predictedx
. e.g.-KC(Ax + Bu)
Why does I never see notifications?
Anyway, state update can be written like this:
x = x + K(difference)
If we say that difference = ymeasurement - yhat= y - Cx
and next state update is x = Ax + Bu
. Then we can write the observer like this:
dx = Ax + Bu + K(y - Cx)
This is the same way as writing as this:
dx = (A - KC)x + Bu + Ky
And it's the same way for simplification:
dx = Ax - KCx + Bu + Ky
By the way. I recommend Unscented Kalman Filter every day over a linear Kalman Filter. The linear Kalman Filter works good, but Unscented Kalman Filter works even better for all systems.
If you don't know the models of your system and you only want to filter it. Then you can use a random gaussian variable instead. See STM32-Libraries repository -> VLX6180 laser sensor measurement.
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Related Issues (6)
- Missing License File HOT 1
- eig.c function, indexing error? HOT 11
- std function causes redefinition error while linking HOT 1
- Cat function HOT 4
- Inverse of a matrix fails HOT 4
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