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The MCvKalman type exposes the following members.

Fields

  NameDescription
Public fieldcontrol_matrix
control matrix (B) (it is not used if there is no control)
Public fieldCP
number of control vector dimensions
Public fieldDP
number of state vector dimensions
Public fieldDynamMatr
=transition_matrix->data.fl
Public fielderror_cov_post
posteriori error estimate covariance matrix P(k)=(I-K(k)*H)*P'(k)
Public fielderror_cov_pre
priori error estimate covariance matrix P'(k)=A*P(k-1)*At + Q)
Public fieldgain
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Public fieldKalmGainMatr
=gain->data.fl
Public fieldmeasurement_matrix
measurement matrix (H)
Public fieldmeasurement_noise_cov
measurement noise covariance matrix (R)
Public fieldMeasurementMatr
=measurement_matrix->data.fl
Public fieldMNCovariance
=measurement_noise_cov->data.fl
Public fieldMP
number of measurement vector dimensions
Public fieldPNCovariance
=process_noise_cov->data.fl
Public fieldPosterErrorCovariance
=error_cov_post->data.fl
Public fieldPosterState
=state_pre->data.fl
Public fieldPriorErrorCovariance
=error_cov_pre->data.fl
Public fieldPriorState
=state_post->data.fl
Public fieldprocess_noise_cov
process noise covariance matrix (Q)
Public fieldstate_post
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Public fieldstate_pre
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Public fieldtemp1
temporary matrices
Public fieldTemp1Data
temp1->data.fl
Public fieldtemp2
temporary matrices
Public fieldTemp2Data
temp2->data.fl
Public fieldtemp3
temporary matrices
Public fieldtemp4
temporary matrices
Public fieldtemp5
temporary matrices
Public fieldtransition_matrix
state transition matrix (A)

See Also