Path: Common/Estimation
% Extended Kalman Filter prediction step.
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Form:
d = EKFPredict( d )
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Inputs
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d (.) EKF data structure
.m (n,1) Mean
.p (n,n) Covariance
.q (n,n) State noise
.f (1,:) Name of right hand side
.fX (1,:) Jacobian of right hand side
.fData (1,1) Data structure with data for f
.dT (1,1) Time step
.t (1,1) Time
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Outputs
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d (.) EKF data structure with updated fields
.m (n,1) Mean
.p (n,n) Covariance
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Math: Integration/RK4
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