UKFRTSS:

Path: Common/Estimation

% Rauch-Tung-Striebel smoothing for an Unscented Kalman Filter.
 This is for a non-augmented, i.e. additive Gaussian noise form.
 This assumes a model of the form:

 x = f(x,q)
 y = h(x,r)

 You pass the data structure that was already generated by
 an Unscented Kalman Filter and the results of the predict and update
 steps.

--------------------------------------------------------------------------
   Form:
   d = UKFRTSS( m, p, d )
--------------------------------------------------------------------------

   ------
   Inputs
   ------
   m   (n,:)   Means
   p   (n,n,:) Covariance
   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,:)          Name of right hand side
               .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
               .mS      (:,1)          Mean smoothed
               .pS      (:,:)          Covariance smoothed

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

Children:

Math: Integration/RK4
Math: Linear/DupVect

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