opencv  2.2.0
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cv::KalmanFilter Class Reference

#include <tracking.hpp>

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 ()
 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
 
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

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

measurement matrix (H)

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: