wiener(1)

NAME
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.