CvKalman Properties OpenCvSharp Class Library

The CvKalman type exposes the following members.

Properties

  NameDescription
Protected propertyAllocatedMemory
Gets or sets a memory address allocated by AllocMemory.
(Inherited from DisposableObject.)
Protected propertyAllocatedMemorySize
Gets or sets the byte length of the allocated memory
(Inherited from DisposableObject.)
Public propertyControlMatrix
Control matrix (B) (it is not used if there is no control
Public propertyCP
Number of control vector dimensions
Public propertyCvPtr
Native pointer of OpenCV structure
(Inherited from DisposableCvObject.)
Public propertyDP
Number of state vector dimensions
Public propertyDynamMatr
transition_matrix->data.fl
Public propertyErrorCovPost
Posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
Public propertyErrorCovPre
Priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
Public propertyGain
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Public propertyIsDisposed
Gets a value indicating whether this instance has been disposed.
(Inherited from DisposableObject.)
Public propertyIsEnabledDispose
Gets or sets a value indicating whether you permit disposing this instance.
(Inherited from DisposableObject.)
Public propertyKalmGainMatr
gain->data.fl
Public propertyMeasurementMatr
measurement_matrix->data.fl
Public propertyMeasurementMatrix
Measurement matrix (H)
Public propertyMeasurementNoiseCov
Measurement noise covariance matrix (R)
Public propertyMNCovariance
measurement_noise_cov->data.fl
Public propertyMP
Number of measurement vector dimensions
Public propertyPNCovariance
process_noise_cov->data.fl
Public propertyPosterErrorCovariance
error_cov_post->data.fl
Public propertyPosterState
state_pre->data.fl
Public propertyPriorErrorCovariance
error_cov_pre->data.fl
Public propertyPriorState
state_post->data.fl
Public propertyProcessNoiseCov
Process noise covariance matrix (Q)
Public propertyStatePost
Corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Public propertyStatePre
Predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Public propertyTemp1
Temporary matrix 1
Public propertyTemp1Data
temp1->data.fl
Public propertyTemp2
Temporary matrix 2
Public propertyTemp2Data
temp2->data.fl
Public propertyTemp3
Temporary matrix 3
Public propertyTemp4
Temporary matrix 4
Public propertyTemp5
Temporary matrix 5
Public propertyTransitionMatrix
State transition matrix (A)
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