cv::KalmanFilter Class Reference

#include <tracking.hpp>

List of all members.

Public Member Functions

CV_WRAP const Matcorrect (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 Matpredict (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)

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

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

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.

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

computes predicted state


Member Data Documentation

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)


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