#include <tracking.hpp>
Public Member Functions | |
CV_WRAP const Mat & | correct (const Mat &measurement) |
updates the predicted state from the measurement | |
void | init (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F) |
re-initializes Kalman filter. The previous content is destroyed. | |
CV_WRAP | KalmanFilter (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F) |
the full constructor taking the dimensionality of the state, of the measurement and of the control vector | |
CV_WRAP | KalmanFilter () |
the default constructor | |
CV_WRAP const Mat & | predict (const Mat &control=Mat()) |
computes predicted state | |
Public Attributes | |
Mat | controlMatrix |
control matrix (B) (not used if there is no control) | |
Mat | errorCovPost |
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) | |
Mat | errorCovPre |
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ | |
Mat | gain |
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R). | |
Mat | measurementMatrix |
measurement matrix (H) | |
Mat | measurementNoiseCov |
measurement noise covariance matrix (R) | |
Mat | processNoiseCov |
process noise covariance matrix (Q) | |
Mat | statePost |
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) | |
Mat | statePre |
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) | |
Mat | temp1 |
Mat | temp2 |
Mat | temp3 |
Mat | temp4 |
Mat | temp5 |
Mat | transitionMatrix |
state transition matrix (A) |
Kalman filter.
The class implements standard Kalman filter {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.
CV_WRAP cv::KalmanFilter::KalmanFilter | ( | ) |
the default constructor
CV_WRAP cv::KalmanFilter::KalmanFilter | ( | int | dynamParams, | |
int | measureParams, | |||
int | controlParams = 0 , |
|||
int | type = CV_32F | |||
) |
the full constructor taking the dimensionality of the state, of the measurement and of the control vector
updates the predicted state from the measurement
void cv::KalmanFilter::init | ( | int | dynamParams, | |
int | measureParams, | |||
int | controlParams = 0 , |
|||
int | type = CV_32F | |||
) |
re-initializes Kalman filter. The previous content is destroyed.
computes predicted state
control matrix (B) (not used if there is no control)
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R).
measurement matrix (H)
measurement noise covariance matrix (R)
process noise covariance matrix (Q)
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
state transition matrix (A)