Class KalmanFilter
- Namespace
- OpenCvSharp
- Assembly
- OpenCvSharp.dll
Kalman filter. The class implements standard Kalman filter \url{http://en.wikipedia.org/wiki/Kalman_filter}. However, you can modify KalmanFilter::transitionMatrix, KalmanFilter::controlMatrix and KalmanFilter::measurementMatrix to get the extended Kalman filter functionality.
public class KalmanFilter : CvObject, IDisposable- Inheritance
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KalmanFilter
- Implements
- Inherited Members
Constructors
- KalmanFilter()
the default constructor
- KalmanFilter(int, int, int, int)
the full constructor taking the dimensionality of the state, of the measurement and of the control vector
Properties
- ControlMatrix
control matrix (B) (not used if there is no control)
- ErrorCovPost
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
- ErrorCovPre
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)At + Q)/
- Gain
Kalman gain matrix (K(k)): K(k)=P'(k)Htinv(H*P'(k)*Ht+R)
- MeasurementMatrix
measurement matrix (H)
- MeasurementNoiseCov
measurement noise covariance matrix (R)
- ProcessNoiseCov
process noise covariance matrix (Q)
- StatePost
corrected state (x(k)): x(k)=x'(k)+K(k)(z(k)-Hx'(k))
- StatePre
predicted state (x'(k)): x(k)=Ax(k-1)+Bu(k)
- TransitionMatrix
state transition matrix (A)
Methods
- Correct(Mat)
updates the predicted state from the measurement
- DisposeUnmanaged()
Releases unmanaged resources
- Init(int, int, int, int)
re-initializes Kalman filter. The previous content is destroyed.
- Predict(Mat?)
computes predicted state