cv::KalmanFilter Class Reference

#include <tracking.hpp>

Public Member Functions
	KalmanFilter ()
	the default constructor More...
	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 Mat &	predict (const Mat &control=Mat())
	computes predicted state More...
const Mat &	correct (const Mat &measurement)
	updates the predicted state from the measurement More...

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

KalmanFilter() [1/2]

cv::KalmanFilter::KalmanFilter

(

)

the default constructor

KalmanFilter() [2/2]

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

correct()

const Mat& cv::KalmanFilter::correct

(

const Mat &

measurement

)

updates the predicted state from the measurement

init()

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

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

predict()

const Mat& cv::KalmanFilter::predict

(

const Mat &

control = Mat()

)

computes predicted state

Member Data Documentation

controlMatrix

Mat cv::KalmanFilter::controlMatrix

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

errorCovPost

Mat cv::KalmanFilter::errorCovPost

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

errorCovPre

Mat cv::KalmanFilter::errorCovPre

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

gain

Mat cv::KalmanFilter::gain

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

measurementMatrix

Mat cv::KalmanFilter::measurementMatrix

measurement matrix (H)

measurementNoiseCov

Mat cv::KalmanFilter::measurementNoiseCov

measurement noise covariance matrix (R)

processNoiseCov

Mat cv::KalmanFilter::processNoiseCov

process noise covariance matrix (Q)

statePost

Mat cv::KalmanFilter::statePost

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

statePre

Mat cv::KalmanFilter::statePre

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

temp1

Mat cv::KalmanFilter::temp1

temp2

Mat cv::KalmanFilter::temp2

temp3

Mat cv::KalmanFilter::temp3

temp4

Mat cv::KalmanFilter::temp4

temp5

Mat cv::KalmanFilter::temp5

transitionMatrix

Mat cv::KalmanFilter::transitionMatrix

state transition matrix (A)

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

• tracking.hpp