Path: Common/Estimation
% Unscented Kalman Filter state prediction step.
This uses RK4 to propagate the state.
Use d = UKFWeight( d ) to get the weight matrices.
The function f is of the form f(x,t,d) where d is a data structure
contained in fData.
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Form:
d = UKFPredict( d )
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Inputs
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d (1,1) UKF data structure
.m (n,1) Mean
.p (n,n) Covariance
.q (n,n) State noise
.wM (1,2n+1) Model weights
.w (2n+1,2n+1) Weight matrix
.f (1,:) Pointer for the right hand side
function
.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 (1,1) UKF data structure
.m (:,1) Mean
.p (:,:) Covariance
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Math: Integration/RK4 Math: Linear/DupVect
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