EKFUpdate:

Path: Common/Estimation

% Extended Kalman Filter measurement update step.

 All inputs are after the predict state (see EKFPredict). The h
 data field may contain either a function name for computing
 the estimated measurements or an m by n matrix. If h is a function
 name you must include hX which is a function to compute the m by n
 matrix is a linearized version of the function h.

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   Form:
   d = EKFUpdate( d )
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   ------
   Inputs
   ------
   d	(1,1)  EKF data structure
              .m       (n,1)	Mean
              .p       (n,n)	Covariance
              .h       (m,n)	Either matrix or name of function
              .hX      (1,:)	Name of Jacobian for h
              .y       (m,1) Measurement vector
              .r       (m,m)	Measurement covariance vector
              .hData   (1,1)	Datastructure for the h and hX functions

   -------
   Outputs
   -------
   d	(1,1)  EKF data structure
              .m       (n,1)	Mean
              .p       (n,n)	Covariance
              .v       (m,1)	Residuals

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