Kalman filter class.
More...
#include "tracking.hpp"
Kalman filter class.
The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [133] . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality. See the OpenCV sample kalman.cpp.
- Note
- An example using the standard Kalman filter can be found at opencv_source_code/samples/cpp/kalman.cpp
cv::KalmanFilter::KalmanFilter |
( |
| ) |
|
The constructors.
- Note
- In C API when CvKalman* kalmanFilter structure is not needed anymore, it should be released with cvReleaseKalman(&kalmanFilter)
cv::KalmanFilter::KalmanFilter |
( |
int |
dynamParams, |
|
|
int |
measureParams, |
|
|
int |
controlParams = 0 , |
|
|
int |
type = CV_32F |
|
) |
| |
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
- Parameters
-
dynamParams | Dimensionality of the state. |
measureParams | Dimensionality of the measurement. |
controlParams | Dimensionality of the control vector. |
type | Type of the created matrices that should be CV_32F or CV_64F. |
const Mat& cv::KalmanFilter::correct |
( |
const Mat & |
measurement | ) |
|
Updates the predicted state from the measurement.
- Parameters
-
measurement | The measured system parameters |
void cv::KalmanFilter::init |
( |
int |
dynamParams, |
|
|
int |
measureParams, |
|
|
int |
controlParams = 0 , |
|
|
int |
type = CV_32F |
|
) |
| |
Re-initializes Kalman filter. The previous content is destroyed.
- Parameters
-
dynamParams | Dimensionality of the state. |
measureParams | Dimensionality of the measurement. |
controlParams | Dimensionality of the control vector. |
type | Type of the created matrices that should be CV_32F or CV_64F. |
const Mat& cv::KalmanFilter::predict |
( |
const Mat & |
control = Mat() | ) |
|
Computes a predicted state.
- Parameters
-
control | The optional input control |
Mat cv::KalmanFilter::controlMatrix |
control matrix (B) (not used if there is no control)
Mat cv::KalmanFilter::errorCovPost |
posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
Mat cv::KalmanFilter::errorCovPre |
priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
Mat cv::KalmanFilter::gain |
Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
Mat cv::KalmanFilter::measurementMatrix |
Mat cv::KalmanFilter::measurementNoiseCov |
measurement noise covariance matrix (R)
Mat cv::KalmanFilter::processNoiseCov |
process noise covariance matrix (Q)
Mat cv::KalmanFilter::statePost |
corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat cv::KalmanFilter::statePre |
predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat cv::KalmanFilter::temp1 |
Mat cv::KalmanFilter::temp2 |
Mat cv::KalmanFilter::temp3 |
Mat cv::KalmanFilter::temp4 |
Mat cv::KalmanFilter::temp5 |
Mat cv::KalmanFilter::transitionMatrix |
state transition matrix (A)
The documentation for this class was generated from the following file: