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Aide Scilab >> Traitement du Signal > 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

Authors

C. B.

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Last updated:
Wed Oct 05 12:10:57 CEST 2011