CDKF:

Path: Common/Estimation

% Continuous discrete iterated extended Kalman Filter.
 The state and covariance matrices are numerically integrated.
 The model is of the form:

   dx/dt = f(x,t,u)
       y = h(x,t,u)

   F(x,t,u) = df/dx
   H(x,t)   = dh/dx

 x is the estimated state. Name is a function of the form:

  [xDot, d.fData] = fName( x, t, d.fData )

 hName is a function of the form:

  [z, dhdx] = hName( x, t, d.hData, d.meas.k )
 
 If dhdx is empty, CDKF will linearize hName about x and t. k are the indices
 to the measurements for which there is data.

--------------------------------------------------------------------------
   Form:
   d = CDKF( d, fName, hName )
--------------------------------------------------------------------------

   ------
   Inputs
   ------
   d			  (:)     Data structure
                         .time             Time or jD
                               .year
                               .month                             
                               .day                             
                               .hour                             
                               .minute                             
                               .second
                         .timeLast         Time of last call
                         .secFromStart     Seconds from start
                         .x                State
                         .p                Covariance
                         .q                Plant noise matrix
                         .k                Gain matrix
                         .r                Measurement noise matrix
                         .nIterations      Number of iterations
                         .meas             Measurements
                                .z           New measurement(s)
                                .k           Indices of new measurement(s)
                         .fData            f function data
                                (user defined)
                         .hData            h function data
                                (user defined)
                
   fName		  (1,:)   Dynamics function name
   hName		  (1,:)   Measurement function name

   -------
   Outputs
   -------
   d			  (:)     Data structure

--------------------------------------------------------------------------
   References: Gelb, A. Ed., Applied Optimal Estimation, MIT Press. p.188. 
               Table 6.1-1. Also, pp. 190-191.
--------------------------------------------------------------------------

Children:

Common: Time/Date2JD
Math: Analysis/Jacobian

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