Kalman filtering

[thom]

Hi,

I have just studied Kalman filtering algorithm to reduce noise, and I hav
seen that a state transition matrix, the channel distortion matrix, th
covariance matrix of the state equation input and the covariance matrix o
the additive noise are needed...
Do you know if there is a mean to use the algorithm without thes
parameters? In my opinion there are cases for which it's not possible t
model the channel distortions or the transition between a sample x(n-1
and a sample x(n).

[Setanta]

"thom" <[email protected]> wrote in message
news:[email protected] ...
> Hi,
>
> I have just studied Kalman filtering algorithm to reduce noise, and I have
> seen that a state transition matrix, the channel distortion matrix, the
> covariance matrix of the state equation input and the covariance matrix of
> the additive noise are needed...
> Do you know if there is a mean to use the algorithm without these
> parameters? In my opinion there are cases for which it's not possible to
> model the channel distortions or the transition between a sample x(n-1)
> and a sample x(n).

it all depends upon the original assumptions you are making - and if this
can be predicted with a degree of certainty in the actual process

[naebad]

The extened Kalman filter but it doesn't always work since it is
non-linear.
In many cases you can get the mathematical state equations before hand
and make an estimate of the noise if it is stationary. If it isn't then
you may as well go back to LMS methods.

Tam

[Setanta]

"naebad" <[email protected]> wrote in message
news:[email protected] ups.com...
> The extened Kalman filter but it doesn't always work since it is
> non-linear.
> In many cases you can get the mathematical state equations before hand
> and make an estimate of the noise if it is stationary. If it isn't then
> you may as well go back to LMS methods.
>
> Tam
>

Is it impossible to predict non-staionarity in noise data - through
adaptation -in a similar way to any adaptive structure?

Perhaps it isnt practical?