[Bayes++] Re: bayes++...

[Michael S. <ma...@mi...>]

On Freitag 23 September 2005 20:01, Baskar Jayaraman wrote:
> Hi Michael, I am trying to use bayes++ for solving a linear state
> estimation problem using (i) a direct kalman filter and (ii) indirect error
> filter. I am getting different results. Indirect error filter appears to
> produce better results. To understand the results better, do you have a
> reference for this filter?

I assume you are refereing to the indirect error filter as implemented in 
'BayesFilter/filters/indirect.hpp' ?

For a linear problem the results should be mathematically identical. The 
numerics may be slightly different particularly if your problem is scaled 
badly so that very large and small numbers are added. This seems unlikely in 
your model.

With regard to references I think the most common Book reference is:
Maybeck, P., 1979, "Stochastic models, estimation,
and control" Volume 1, Academic Press, New York.
Google 'indriect kalman filter' comes up with a number of papers although I 
could not find anything which gives a good introduction.

> Also, if you know of any user discussion group 
> for bayes++ please let me know so that I can learn from other people's
> experience.

There is a 'bay...@li...' ML which I have 
forwarded my reply to.

> FYI, the model of my system is 
>   x1(k+1) = x1(k) + u1(k)-u2(k)+w1(k)
> x2(k+1) = x2(k) + u2(k)-u3(k)+w2(k)
> y(k) = x1(k) + x2(k) + v(k)
>  w1,w2,v are noises. u1, u2, u3 are control actions. y is the observation
> and I need to estimate the two states x1 and x2. The problem is somewhat
> tricky because there are some errors in the control actions u1,u2,u3
> because of actuator errors. for example, I tell the control system to
> implement a control action of value of say 5 but what really goes into the
> process is something else, say 6. I am modeling these errors as being
> included in w1 and w2.

Looks fine,
	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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