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], 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