#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)
1.7.2