KFExItMu:

--------------------------------------------------------------------------
   One step of the extended iterated Kalman Filter measurement
   update. Also can include a forgetting factor to keep the covariance
   from going to zero. This can be used independently if the
   state is constant. This can also be used to process measurements
   one at a time by passing different hname and Hname with each call
--------------------------------------------------------------------------
   Form:
   [x, P, K] = KFExItMu( hname, Hname, H, Hindex, R, z, x, P, f, nits, u )
--------------------------------------------------------------------------

   ------
   Inputs
   ------
   hname                  The name of the function that supplies the
                          constant term in the measurement equation
   Hname                  The name of the function that supplies the
                          partials in the measurement equation
   H                      Dummy H vector (for sizing only)
   Hindex                 The location of Hname in H
   R                      Measurement covariance matrix
   z                      Measurement vector
   x                      State
   P                      Covariance matrix
   f                      Forgetting factor (0 to 1)
   nits                   Maximum number of iterations
   u                      Auxiliary vector to pass to hname and Hname

   -------
   Outputs
   -------
   x                      State
   P                      Covariance matrix
   K                      Gain matrix

--------------------------------------------------------------------------
	References: Gelb, A., Applied Optimal Estimation, MIT Press, Cambridge,
               1974, pp. 190-191.
--------------------------------------------------------------------------