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Manual Scilab >> Processamento de Sinais > wiener

# wiener

Wiener estimate

### Calling Sequence

`[xs,ps,xf,pf]=wiener(y,x0,p0,f,g,h,q,r)`

### Arguments

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

C. B.

### Comments

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