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 oclMat &	predict (const oclMat &control=oclMat())
const oclMat &	correct (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

cv::ocl::KalmanFilter::KalmanFilter

(

)

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

const oclMat& cv::ocl::KalmanFilter::correct

(

const oclMat &

measurement

)

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.

const oclMat& cv::ocl::KalmanFilter::predict

(

const oclMat &

control = oclMat()

)

Member Data Documentation

oclMat cv::ocl::KalmanFilter::controlMatrix

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

oclMat cv::ocl::KalmanFilter::errorCovPost

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

oclMat cv::ocl::KalmanFilter::errorCovPre

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

oclMat cv::ocl::KalmanFilter::gain

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

oclMat cv::ocl::KalmanFilter::measurementMatrix

measurement matrix (H)

oclMat cv::ocl::KalmanFilter::measurementNoiseCov

measurement noise covariance matrix (R)

oclMat cv::ocl::KalmanFilter::processNoiseCov

process noise covariance matrix (Q)

oclMat cv::ocl::KalmanFilter::statePost

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

oclMat cv::ocl::KalmanFilter::statePre

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

oclMat cv::ocl::KalmanFilter::transitionMatrix

state transition matrix (A)

---

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

• ocl/include/opencv2/ocl/ocl.hpp