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