CovarianceRHS:
-------------------------------------------------------------------------------
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 )
-------------------------------------------------------------------------------
Form:
pDot = CovarianceRHS( t, p, flag, d )
-------------------------------------------------------------------------------
------
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
-------------------------------------------------------------------------------
References: Gelb, A. Ed., Applied Optimal Estimation, MIT Press. p.188.
Table 6.1-1. Also, pp. 190-191.
-------------------------------------------------------------------------------
Children:
Common: Math/JacobianODE