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.TH wiener 1 "April 1993" "Scilab Group" "Scilab Function"
.so ../sci.an
.SH NAME
wiener - Wiener estimate
.SH CALLING SEQUENCE
.nf
[xs,ps,xf,pf]=wiener(y,x0,p0,f,g,h,q,r)
.fi
.SH PARAMETERS
.TP 10
f, g, h
: system matrices in the interval \fV[t0,tf]\fR
.RS
.TP
f
=\fV[f0,f1,...,ff]\fR, and \fVfk\fR is a nxn matrix
.TP
g
=\fV[g0,g1,...,gf]\fR, and \fVgk\fR is a nxn matrix
.TP
h
=\fV[h0,h1,...,hf]\fR, and \fVhk\fR is a mxn matrix
.RE
.TP
q, r
: covariance matrices of dynamics and observation noise
.RS
.TP
q
=\fV[q0,q1,...,qf]\fR, and \fVqk\fR is a nxn matrix
.TP
r
=\fV[r0,r1,...,rf]\fR, and \fVgk\fR is a mxm matrix
.RE
.TP
x0, p0
: initial state estimate and error variance
.TP
y
: observations in the interval \fV[t0,tf]\fR.
\fVy=[y0,y1,...,yf]\fR, and \fVyk\fR is a column m-vector
.TP 10
xs
: Smoothed state estimate
\fVxs= [xs0,xs1,...,xsf]\fR, and \fVxsk\fR is a column n-vector
.TP
ps
: Error covariance of smoothed estimate
\fVps=[p0,p1,...,pf]\fR, and \fVpk\fR is a nxn matrix
.TP
xf
: Filtered state estimate
\fVxf= [xf0,xf1,...,xff]\fR, and \fVxfk\fR is a column n-vector
.TP
pf
: Error covariance of filtered estimate
\fVpf=[p0,p1,...,pf]\fR, and \fVpk\fR is a nxn matrix
.SH DESCRIPTION
function which gives the Wiener estimate using
the forward-backward Kalman filter formulation
.SH AUTHOR
C. B.
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