frc::MerweScaledSigmaPoints< States > Class Template Reference

Generates sigma points and weights according to Van der Merwe's 2004 dissertation[1] for the UnscentedKalmanFilter class. More...

#include <frc/estimator/MerweScaledSigmaPoints.h>

Public Member Functions
	MerweScaledSigmaPoints (double alpha=1e-3, double beta=2, int kappa=3 - States)
	Constructs a generator for Van der Merwe scaled sigma points.
int	NumSigmas ()
	Returns number of sigma points for each variable in the state x.
Matrixd< States, 2 *States+1 >	SquareRootSigmaPoints (const Vectord< States > &x, const Matrixd< States, States > &S)
	Computes the sigma points for an unscented Kalman filter given the mean (x) and square-root covariance (S) of the filter.
const Vectord< 2 *States+1 > &	Wm () const
	Returns the weight for each sigma point for the mean.
double	Wm (int i) const
	Returns an element of the weight for each sigma point for the mean.
const Vectord< 2 *States+1 > &	Wc () const
	Returns the weight for each sigma point for the covariance.
double	Wc (int i) const
	Returns an element of the weight for each sigma point for the covariance.

Detailed Description

template<int States>
class frc::MerweScaledSigmaPoints< States >

Generates sigma points and weights according to Van der Merwe's 2004 dissertation[1] for the UnscentedKalmanFilter class.

It parametrizes the sigma points using alpha, beta, kappa terms, and is the version seen in most publications. Unless you know better, this should be your default choice.

[1] R. Van der Merwe "Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models" (Doctoral dissertation)

Template Parameters

States

The dimensionality of the state. 2 * States + 1 weights will be generated.

Constructor & Destructor Documentation

MerweScaledSigmaPoints()

template<int States>

frc::MerweScaledSigmaPoints< States >::MerweScaledSigmaPoints ( double alpha = 1e-3, double beta = 2, int kappa = 3 - States )

inlineexplicit

Constructs a generator for Van der Merwe scaled sigma points.

Parameters

alpha	Determines the spread of the sigma points around the mean. Usually a small positive value (1e-3).
beta	Incorporates prior knowledge of the distribution of the mean. For Gaussian distributions, beta = 2 is optimal.
kappa	Secondary scaling parameter usually set to 0 or 3 - States.

Member Function Documentation

NumSigmas()

template<int States>

int frc::MerweScaledSigmaPoints< States >::NumSigmas ( )

inline

Returns number of sigma points for each variable in the state x.

SquareRootSigmaPoints()

template<int States>

Matrixd< States, 2 *States+1 > frc::MerweScaledSigmaPoints< States >::SquareRootSigmaPoints ( const Vectord< States > & x, const Matrixd< States, States > & S )

inline

Computes the sigma points for an unscented Kalman filter given the mean (x) and square-root covariance (S) of the filter.

Parameters

x	An array of the means.
S	Square-root covariance of the filter.

Returns

Two dimensional array of sigma points. Each column contains all of the sigmas for one dimension in the problem space. Ordered by Xi_0, Xi_{1..n}, Xi_{n+1..2n}.

Wc() [1/2]

template<int States>

const Vectord< 2 *States+1 > & frc::MerweScaledSigmaPoints< States >::Wc ( ) const

inline

Returns the weight for each sigma point for the covariance.

Wc() [2/2]

template<int States>

double frc::MerweScaledSigmaPoints< States >::Wc ( int i ) const

inline

Returns an element of the weight for each sigma point for the covariance.

Parameters

i	Element of vector to return.

Wm() [1/2]

template<int States>

const Vectord< 2 *States+1 > & frc::MerweScaledSigmaPoints< States >::Wm ( ) const

inline

Returns the weight for each sigma point for the mean.

Wm() [2/2]

template<int States>

double frc::MerweScaledSigmaPoints< States >::Wm ( int i ) const

inline

Returns an element of the weight for each sigma point for the mean.

Parameters

i	Element of vector to return.

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

• frc/estimator/MerweScaledSigmaPoints.h