KalmanFilter< N > Class Template Reference

Kalman filter for efficient and optimal velocity estimation based on (noisy) position data with possibly noisy timing. More...

#include <KalmanFilter.h>

List of all members.

Public Member Functions
	KalmanFilter ()
void	init (double *y0, double w=30, double v=0.0005, double dt=0.001)
	initialize fresh
void	update (double *y)
	Update Kalman filter with new measurement and standard dt.
void	update (double *y, double dt)
	Update Kalman filter with new measurement and dt.
void	velocity (double *v)
	Return filtered velocity.
void	position (double *p)
	Return filtered position.
Public Attributes
Vector2D	x [N]
	Current state on nth channel.
Vector2D	xm
	current state after time update
Vector2D	K
	Current Kalman gain.
Matrix2D	P
	Current uncertainty matrix for all channels.
Matrix2D	Pm
	Current uncertainty matrix after time update.
Matrix2D	A
	Transition matrix.
Matrix2D	Q
	Conditional uncertainty matrix.
double	w2
	Variance of state transitions.
double	v2
	Measurement variance.
double	dt
double	dt2
double	dt3
	delta t and powers of

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Detailed Description

template<int N>
class KalmanFilter< N >

Kalman filter for efficient and optimal velocity estimation based on (noisy) position data with possibly noisy timing.

Template class for n-dimensional position data. All channels are treated as being independent and equal in variance and dt. Kalman filter assumes that acceleration is a Gaussian White noise process (velocity is brownian motion) . We thank Daniel Wolpert for pointing us to this method.

Author:

Joern Diedrichsen

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Constructor & Destructor Documentation

template<int N>

KalmanFilter< N >::KalmanFilter

(

)

N is the number of channels

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Member Function Documentation

template<int N>

void KalmanFilter< N >::init	(	double *	x0,
		double	W = 30,
		double	V = 0.0005,
		double	DT = 0.001
	)

initialize fresh

Parameters:

*x0	array of initial states of the the channels
W	standard devation of the prior on the accelerations
V	noise standard deviation of the position observations
DT	standard delta-t, in case you want to precompute Kalman gains

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

• KalmanFilter.h