CovarianceRHS:

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   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, flag, d )
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   ------
   Inputs
   ------
   t       (1,1)   Time
   p       (:,1)   Covariance matrix
   flag    (1,:)   Flag for ode113
   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.
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Children:

Common: Math/JacobianODE