Scilab Function
Last update : 12/1/2004
wiener - Wiener estimate
Calling Sequence
- [xs,ps,xf,pf]=wiener(y,x0,p0,f,g,h,q,r)
Parameters
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f, g, h: system matrices in the interval [t0,tf]
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f=[f0,f1,...,ff], and fk is a nxn matrix
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g=[g0,g1,...,gf], and gk is a nxn matrix
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h=[h0,h1,...,hf], and hk is a mxn matrix
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q, r: covariance matrices of dynamics and observation noise
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q=[q0,q1,...,qf], and qk is a nxn matrix
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r=[r0,r1,...,rf], and gk is a mxm matrix
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x0, p0: initial state estimate and error variance
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y: observations in the interval [t0,tf]. y=[y0,y1,...,yf], and yk is a column m-vector
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xs: Smoothed state estimate xs= [xs0,xs1,...,xsf], and xsk is a column n-vector
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ps: Error covariance of smoothed estimate ps=[p0,p1,...,pf], and pk is a nxn matrix
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xf: Filtered state estimate xf= [xf0,xf1,...,xff], and xfk is a column n-vector
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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.