Man Scilab
wiener
Scilab Function
wiener - Wiener estimate
Calling Sequence
-
[xs,ps,xf,pf]=wiener(y,x0,p0,f,g,h,q,r)
Parameters
-
f, g, h
: system matrices in the interval
[t0,tf]
-
f
=
[f0,f1,...,ff]
, and
fk
is a nxn matrix
-
g
=
[g0,g1,...,gf]
, and
gk
is a nxn matrix
-
h
=
[h0,h1,...,hf]
, and
hk
is a mxn matrix
-
q, r
: covariance matrices of dynamics and observation noise
-
q
=
[q0,q1,...,qf]
, and
qk
is a nxn matrix
-
r
=
[r0,r1,...,rf]
, and
gk
is a mxm matrix
-
x0, p0
: initial state estimate and error variance
-
y
: observations in the interval
[t0,tf]
.
y=[y0,y1,...,yf]
, and
yk
is a column m-vector
-
xs
: Smoothed state estimate
xs= [xs0,xs1,...,xsf]
, and
xsk
is a column n-vector
-
ps
: Error covariance of smoothed estimate
ps=[p0,p1,...,pf]
, and
pk
is a nxn matrix
-
xf
: Filtered state estimate
xf= [xf0,xf1,...,xff]
, and
xfk
is a column n-vector
-
pf
: Error covariance of filtered estimate
pf=[p0,p1,...,pf]
, and
pk
is a nxn matrix
Description
function which gives the Wiener estimate using
the forward-backward Kalman filter formulation
Author
C. B.
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