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Hi,
can anybody explain what Kalman filters are for ?

Where can I use them ?
What is the benefit of a Kalman filter ?
Are there any disadvantages ?

Thanks in advance
Oliver

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OZ wrote:
> Hi,
> can anybody explain what Kalman filters are for ?
> 
> Where can I use them ?
> What is the benefit of a Kalman filter ?
> Are there any disadvantages ?

http://www.google.com/search?q=Kalman+filter

Jerry
-- 
Engineering is the art of making what you want from things you can get.
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

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OZ wrote:
> Hi,
> can anybody explain what Kalman filters are for ?
> 
> Where can I use them ?
> What is the benefit of a Kalman filter ?
> Are there any disadvantages ?
> 
> Thanks in advance
> Oliver
> 

Kalman Filters are used for many things.  I suggest that you get a copy 
of "Applied Optimal Estimation" by Gelb, MIT Press.  The book is very 
readable and has many applications.

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"OZ" <o...@web.de> wrote in message
news:4...@news.btx.dtag.de...
> Hi,
> can anybody explain what Kalman filters are for ?
>
> Where can I use them ?
> What is the benefit of a Kalman filter ?
> Are there any disadvantages ?
>
> Thanks in advance
> Oliver
>
Its an extension of Wieners work on random signals + noise. Given a random
signal + noise you can make an estimate of the signal. You get a smaller
mean squared error than a Wiener filter for time-varying cases (for which
the Wiener filter is not supposed to be applicable but is still used as part
of an LMS algorithm or whatever).
You will need a state-space description of your signal model.
Can also be used for state-estimation and state-feedback control - thing
called certainty equivalence. The optimal feedback controller for the
noise-free case  can be used if you include a KF when there is noise.There
are also Extended Kalman filters but they are a bit hit and miss -
non-linear.Can be made to work but I wouldn't go near them.In such cases you
estimate the states at the same time as the system model!

Shytot

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Hi shytot,
thanks for this answer. But I will need some time to understand it. 

Oliver

>>
>Its an extension of Wieners work on random signals + noise. Given a random
>signal + noise you can make an estimate of the signal. You get a smaller
>mean squared error than a Wiener filter for time-varying cases (for which
>the Wiener filter is not supposed to be applicable but is still used as part
>of an LMS algorithm or whatever).
>You will need a state-space description of your signal model.
>Can also be used for state-estimation and state-feedback control - thing
>called certainty equivalence. The optimal feedback controller for the
>noise-free case  can be used if you include a KF when there is noise.There
>are also Extended Kalman filters but they are a bit hit and miss -
>non-linear.Can be made to work but I wouldn't go near them.In such cases you
>estimate the states at the same time as the system model!
>
>Shytot
>
>

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Thanks, I will look for this book

>Kalman Filters are used for many things.  I suggest that you get a copy 
>of "Applied Optimal Estimation" by Gelb, MIT Press.  The book is very 
>readable and has many applications.

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