Kalman Fitler-practical measuring the measurement noise covariance matrix R

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hello all again,

I post one message twenty minutes ago, but it didnt appear in the
forum, really dont know what is the matter... anyway...

I just got into a project about state and parameter estimation for a
BLDC motor using Kalman filter. I have already established the
calculation of KF in Matlab/Simulink. The problem now is to choose the
suitable measurement noise covariance R.

I read some materials and also my boss told me it is possible to
measure R in a practical way, and I assume, maybe I should choose some
motor state or paramter to get both the measurement and the estimation
value, and try to get the measurement variance? that is something
like, var = f(x-\hat x)??

But I really dont know how to apply this kind 'practical way' in
PRACTIC. Anyone knows how to deal with that?? or is there some
materials related in this part.

I appreciate for any help:-)

Emily


Re: Kalman Fitler-practical measuring the measurement noise covariance matrix R



Well, I'm not completely sure but:

R=[Sxx Sxy; Syx Syy]     (in Matlab notation ; means next row)

where Sxy is covariance and is equal 0
as far as we consider only one sensor noise
(R is measurement covariance by definition)
and Sxx is a variance of this noise?

Just set it (on R diagonal) constant and equal to measurement error  =

variance sigma^2.
This is in practice your sensor accuracy (or power 2).

If you generate a sensor noise in your simulation then a and b
are that generated signal variance.

-- =

Mikolaj

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