Kalman filter for an one dimensional constant velocity model. More...

Detailed Description

Kalman filter for an one dimensional constant velocity model.

This class provides a Kalman filter for an one dimensional constant velocity model. Assumes constante time intervals.

Public Member Functions
void	initKF (double initPos, double initVar)
	Initialices the Kalman filter with initPos and initVar.
void	updateKF (double y, double sigmaY, double sigmaV)
	Updates the Kalman Filter and updates the prediction for the next step (see getPred())
void	getPred (double &pos, double &c11, double &c12, double &c22)
	Provides a state prediction with its covariance.
double	getPosPred ()
	Provides a state prediction.
double	getPosVar ()
	Provides the variance of the state prediction.
void	setPosPeriod (double min, double max)
	The posPeriod is an opportunity to bound the state values.
Private Attributes
double	x1
double	x2
	> position
double	cov11
	> velocity
double	cov12
	> variance position
double	cov22
	> correlation
double	x1Pred
	> variance velocity
double	x2Pred
	> position prediction
double	cov11Pred
	> velocity prediction
double	cov12Pred
	> variance of prediction of position
double	cov22Pred
	> correlation of prediction
double	minPeriod
	> variance of prediction of velocity
double	maxPeriod
	> Lower bound for state (priodic!)
bool	periodicFlag
	> Upper bound for state (priodic!)

Member Function Documentation

void jafar::filter::ConstantVelocityKalmanFilter::setPosPeriod	(	double	min,
		double	max
	)		`[inline]`

The posPeriod is an opportunity to bound the state values.

They state space is assumed to be priodic (as it is the case for angles)

void jafar::filter::ConstantVelocityKalmanFilter::updateKF	(	double	y,
		double	sigmaY,
		double	sigmaV
	)

Updates the Kalman Filter and updates the prediction for the next step (see getPred())

Parameters:

y	is the new measurement for the position
sigmaY	is the variance of the measurement
sigmaV	is the process noise and weights the old measurements

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