UKFPredict:

Path: Common/Estimation

% Unscented Kalman Filter state prediction step.

 This uses RK4 to propagate the state.

 Use d = UKFWeight( d ) to get the weight matrices.

 The function f is of the form f(x,t,d) where d is a data structure
 contained in fData.

--------------------------------------------------------------------------
   Form:
   d = UKFPredict( d )
--------------------------------------------------------------------------

   ------
   Inputs
   ------
   d	(1,1)  UKF data structure
              .m       (n,1)       Mean
              .p       (n,n)       Covariance
              .q       (n,n)       State noise
              .wM      (1,2n+1)    Model weights
              .w       (2n+1,2n+1)	Weight matrix
              .f       (1,:)       Pointer for the right hand side
                                   function
              .fData   (1,1)       Data structure with data for f
              .dT      (1,1)       Time step
              .t       (1,1)       Time

   -------
   Outputs
   -------
   d	(1,1)  UKF data structure
              .m       (:,1)       Mean
              .p       (:,:)       Covariance

--------------------------------------------------------------------------

Children:

Math: Integration/RK4
Math: Linear/DupVect

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