KFUpdate:

Path: Common/Estimation

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   Linear Kalman Filter measurement update step.

   This assumes a discrete model of the form:

   x[k] = a[k-1]x[k-1] + b[k-1]u[k-1]
   y[k] = h[k]x[k]

   All inputs are after the predict state (see KFPredict).
   The Kalman filter can be initialized using KFInitialize. This routine
   can use the data structure form of the measurement where the 
   measurement is in y.data.

   The inputs can be individual matrices or as part of a data structure.
   The data structure is d = struct('m',m,'p',p,'y',y,'h',h,'r',r);

   All Kalman Filter routines accept data structures.

   Since version 11.
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   Forms:
   d = KFUpdate( d )
   [m,p,v,s,k] = KFUpdate( m, p, y, h, r )
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   ------
   Inputs
   ------
   m             (n,1)  Mean vector
   p             (n,n)  Covariance matrix
   y             (m,1)  Measurement vector
   h             (m,n)  Measurement matrix
   r             (m,m)  Measurement noise matrix

   -------
   Outputs
   -------
   m             (n,1)  Mean vector
   p             (n,n)  Covariance matrix
   v             (n,n)  Residual vector
   s             (n,n)  Covariance denominator
   k             (n,n)  Kalman gain matrix

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