by using "kalman" function i can calculate Kalman estimator gain of my plant with respect to covariance matrixes Qn and Rn.
kalman(sys,Qn,Rn)

I can define covariance amtrix as
Qn = 2.3
Rn = 1

But what if my input disturbance is "white noise",
how can i change the covariance matrix in this situation??
Can i put white noise to the covarience matrix?

Ben,

Please consider your audience.

If you are not going to take the time to describe your problem clearly and concisely, why would anyone put forth their effort to respond?


Michael.

Hello,

i have a state space model which i call "sys". It has A,B,C,D matrices.
By using Q and R covariance values i use kalman function and calculate static Kalman gain.

Is this gain is for step disturbance effects?



Michael_RW <user@compgroups.net/> wrote in message <0amdnRosy8qnncTWnZ2dnUVZ_oWdnZ2d@giganews.com>...
> 
> Ben,
> 
> Please consider your audience.
> 
> If you are not going to take the time to describe your problem clearly and concisely, why would anyone put forth their effort to respond?
> 
> 
> Michael.
> 
> 
> ---
> frmsrcurl: http://compgroups.net/comp.soft-sys.matlab/kalman-filter-white-noise

Ben,

Without details of the source-code you have written, likely in Matlab, I can not comment more specifically about your state-space model or other aspects of your questions.

With respect to "step disturbance effects", I assume you imply your system is operating at a steady-state and it is then "acted-on" by some external force or control-input, yes?  With respect to, "http://wapedia.mobi/en/Kalman_filter#4.", the filter model will have a control-input component (matrix Bk in the noted reference).

If you have appropriate models for your case (i.e. state-transition model, control-input model and observation model), and reasonable process noise & observation noise  covariances, the gain will be correct.

From your past posts, I assume this is a scalar or one-dimensional Kalman filter application, yes?

Also, keep in mind proper frameworks with respect to underlying Kalman filter assumptions:  Gaussian statistics with linear models;  non-Gaussian statistics with linear models; non-Gaussian statistics and non-linear models.

Two references come to mind...  These involve scalar or one-dimensional Kalman filters with Gaussian statistics and linear models.  I can send these to you if you require additional references.

Michael.