CovarianceRHS:

Path: Math/Probability

% Generates the covariance derivative. 
 The covariance matrix is entered as a column. The columns of the matrix are
 stacked on each other. All off-diagonal terms are propagated.

   noiseFunction( action, tag, d, t )
   fFun( t, x, flag, fData )

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   Form:
   pDot = CovarianceRHS( t, p, d )
--------------------------------------------------------------------------

   ------
   Inputs
   ------
   t       (1,1)   Time
   p       (:,1)   Covariance matrix
   d		    (.)     Data structure
                   .x                State vector
                   .fFun             RHS of the state equations
                   .q                Noise matrix
                   .noiseFunction    Noise matrix function
                   .fData            Data to pass to fFun
                   .qData            Data to pass to qFun
                   .dF               fFun is df/dt
                   .noiseModelOn     1 if noise model is on
                   .noiseFunctionTag Tag to noise function interface
                   .k                Which elements of x to use

   -------
   Outputs
   -------
   pDot   (:,1)   Covariance matrix derivative

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

Children:

Math: Analysis/JacobianODE

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