Man Scilab

wiener - Wiener estimate

Calling Sequence

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

Parameters

• 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

Author

C. B.

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