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Please note that the recommended version of Scilab is 2026.0.0. This page might be outdated.
See the recommended documentation of this function
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], andfkis a nxn matrix- g
=
[g0,g1,...,gf], andgkis a nxn matrix- h
=
[h0,h1,...,hf], andhkis a mxn matrix
- q, r
covariance matrices of dynamics and observation noise
- q
=
[q0,q1,...,qf], andqkis a nxn matrix- r
=
[r0,r1,...,rf], andgkis a mxm matrix
- x0, p0
initial state estimate and error variance
- y
observations in the interval
[t0,tf].y=[y0,y1,...,yf], andykis a column m-vector- xs
Smoothed state estimate
xs= [xs0,xs1,...,xsf], andxskis a column n-vector- ps
Error covariance of smoothed estimate
ps=[p0,p1,...,pf], andpkis a nxn matrix- xf
Filtered state estimate
xf= [xf0,xf1,...,xff], andxfkis a column n-vector- pf
Error covariance of filtered estimate
pf=[p0,p1,...,pf], andpkis a nxn matrix
Description
function which gives the Wiener estimate using the forward-backward Kalman filter formulation
Authors
C. B.
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