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Scilab help >> Signal Processing > filters > sskf

# sskf

steady-state Kalman filter

### Calling Sequence

```xe = sskf(y,f,h,q,r,x0)
[xe, pe]=sskf(y,f,h,q,r,x0)```

### Arguments

y

data in form `[y0,y1,...,yn]`, `yk` a column vector

f

system matrix dim(NxN)

h

observations matrix dim(MxN)

q

dynamics noise matrix dim(NxN)

r

observations noise matrix dim(MxM)

x0

initial state estimate

xe

estimated state

pe

steady-state error covariance

### Description

steady-state Kalman filter

### Examples

```rand("seed",5);
rand("normal");
q=[.03 0.01;.01 0.03];
u=rand(2,11);
f=[1.1 0.1;0 0.8];
g=(chol(q))';
m0=[10 10]';
p0=[2 0;0 2];
x0=m0+(chol(p0))'*rand(2,1);
x=ltitr(f,g,u,x0);
r=[2 0;0 2];
v=(chol(r))'*rand(2,11);
y=x+v;
h=eye(2,2);
[xe pe]=sskf(y,f,h,q,r,m0)```
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