wiener

wiener — Wiener estimate

Calling sequence

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

f, g, h

: system matrices in the interval [t0,tf]

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

function which gives the Wiener estimate using the forward-backward Kalman filter formulation

C. B.