On Tuesday 30 October 2007, Robert Zhang wrote:
> Dear all,
>
> I have read the post name "Covariance matrix Q" in this mail list. I have a
> question on the covariance matrix R which is used in the linearize observe
> model when the EKF is employed. Is it generated similar to Q? However, I do
> not find any Jacobian matrix same as G in the
> "Linrz_uncorrelated_observe_model. Thanks for any help.
The observation covariance matrix R is used directly in Bayes++. For
notational consistency it is call 'Z' in the Bayes++ observe models.
Therefore in 'Linrz_correlated_observe_model has a symetric matrix (SymMatrix)
called 'Z'.
It is very common that the addative noise in observation models are
uncorrelated. In this case 'Z' is a diagonal matrix. You can then use
the 'Linrz_uncorrelated_observe_model' where the vector 'Zv' represents
observation variances.
For example if you have a range+angle measurement (such as a Radar) you can
normally model the measurement as having addative noise in range and addative
noise in angle. These are usually uncorrelated. You can then measure/guess
their variances and place them in Zv[0] and Zb[1].
Regards,
Michael
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Michael Stevens Systems Engineering
34128 Kassel, Germany
Phone/Fax: +49 561 5218038
Navigation Systems, Estimation and
Bayesian Filtering
http://bayesclasses.sf.net
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