OpenCV  2.4.13.2
Open Source Computer Vision
cv::ocl::KalmanFilter Class Reference

#include <ocl.hpp>

Public Member Functions

 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 More...
 
void init (int dynamParams, int measureParams, int controlParams=0, int type=CV_32F)
 re-initializes Kalman filter. The previous content is destroyed. More...
 
const oclMatpredict (const oclMat &control=oclMat())
 
const oclMatcorrect (const oclMat &measurement)
 

Public Attributes

oclMat statePre
 predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) More...
 
oclMat statePost
 corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) More...
 
oclMat transitionMatrix
 state transition matrix (A) More...
 
oclMat controlMatrix
 control matrix (B) (not used if there is no control) More...
 
oclMat measurementMatrix
 measurement matrix (H) More...
 
oclMat processNoiseCov
 process noise covariance matrix (Q) More...
 
oclMat measurementNoiseCov
 measurement noise covariance matrix (R) More...
 
oclMat errorCovPre
 priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/ More...
 
oclMat gain
 Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R) More...
 
oclMat errorCovPost
 posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k) More...
 

Detailed Description

***************Kalman Filter*************!

Constructor & Destructor Documentation

§ KalmanFilter() [1/2]

cv::ocl::KalmanFilter::KalmanFilter ( )

§ KalmanFilter() [2/2]

cv::ocl::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

§ correct()

const oclMat& cv::ocl::KalmanFilter::correct ( const oclMat measurement)

§ init()

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

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

§ predict()

const oclMat& cv::ocl::KalmanFilter::predict ( const oclMat control = oclMat())

Member Data Documentation

§ controlMatrix

oclMat cv::ocl::KalmanFilter::controlMatrix

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

§ errorCovPost

oclMat cv::ocl::KalmanFilter::errorCovPost

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

§ errorCovPre

oclMat cv::ocl::KalmanFilter::errorCovPre

priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/

§ gain

oclMat cv::ocl::KalmanFilter::gain

Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)

§ measurementMatrix

oclMat cv::ocl::KalmanFilter::measurementMatrix

measurement matrix (H)

§ measurementNoiseCov

oclMat cv::ocl::KalmanFilter::measurementNoiseCov

measurement noise covariance matrix (R)

§ processNoiseCov

oclMat cv::ocl::KalmanFilter::processNoiseCov

process noise covariance matrix (Q)

§ statePost

oclMat cv::ocl::KalmanFilter::statePost

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

§ statePre

oclMat cv::ocl::KalmanFilter::statePre

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

§ transitionMatrix

oclMat cv::ocl::KalmanFilter::transitionMatrix

state transition matrix (A)


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