Path: Common/Estimation
% Initializes Kalman Filter data structures for the KF, UKF, EKF and IEKF.
Enter parameter pairs after the type.
If you return with only one input it will return the default data
structure for the filter specified by type. Defaults are returned
for any parameter you do not enter.
The linear system is x[k+1] = ax[k] + bu[k]
y[k] = hx[k]
Nonlinear systems are x = f(x,u), y = h(x,u)
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Form:
d = KFInitialize( type, varargin )
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Inputs
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type (1,1) Type of filter 'ukf', 'kf', 'ekf', 'iekf'
varargin {:} Parameter pairs (not case sensitive)
'a' State transition matrix for KF
'm' 'x' Mean state or state
'b' Input matrix for KF
'u' Inputs
'hX' Pointer to nonlinear measuremeent partials function
'fX' Pointer to nonlinear dynamics partials function for EKF
'f' Pointer to nonlinear dynamics function
'h' Pointer to nonlinear measurement function
'hdata' Data for h
'fdata' Data for f
'p' State covariance matrix
'q' Model covariance matrix
'r' Measurement covariance matrix
'alpha' Scaling parameter for UKF (1e-4 to 1)
'kappa' Scaling parameter for UKF (usually 0)
'beta' Scaling parameter for UKF (usually 2)
'dt' Time step (s)
'nits' Number of iterations for the IEKF
'kmeas' Measurement sets to use for UKF
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Outputs
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d (.) Data structure
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Common: Estimation/UKFWeight
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