cv::KalmanFilter Class Reference

Kalman filter. More...

#include <tracking.hpp>

List of all members.

Public Member Functions

CV_WRAP KalmanFilter ()
 the default constructor
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
void init (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
 re-initializes Kalman filter. The previous content is destroyed.
CV_WRAP const Matpredict (const Mat &control=Mat())
 computes predicted state
CV_WRAP const Matcorrect (const Mat &measurement)
 updates the predicted state from the measurement

Public Attributes

Mat statePre
 predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
Mat statePost
 corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
Mat transitionMatrix
 state transition matrix (A)
Mat controlMatrix
 control matrix (B) (not used if there is no control)
Mat measurementMatrix
 measurement matrix (H)
Mat processNoiseCov
 process noise covariance matrix (Q)
Mat measurementNoiseCov
 measurement noise covariance matrix (R)
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 errorCovPost
 posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
Mat temp1
Mat temp2
Mat temp3
Mat temp4
Mat temp5

Detailed Description

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.


Constructor & Destructor Documentation

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


Member Function Documentation

void cv::KalmanFilter::init ( int  dynamParams,
int  measureParams,
int  controlParams = 0,
int  type = CV_32F 
)

re-initializes Kalman filter. The previous content is destroyed.

CV_WRAP const Mat& cv::KalmanFilter::predict ( const Mat control = Mat()  ) 

computes predicted state

CV_WRAP const Mat& cv::KalmanFilter::correct ( const Mat measurement  ) 

updates the predicted state from the measurement


Member Data Documentation

predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)

corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))

state transition matrix (A)

control matrix (B) (not used if there is no control)

measurement matrix (H)

process noise covariance matrix (Q)

measurement noise covariance matrix (R)

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).

posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)


The documentation for this class was generated from the following file: