SurgSim::Math::KalmanFilter< M, N > Class Template Reference

Implements a linear Kalman filter, a recursive estimator. More...

#include <SurgSim/Math/KalmanFilter.h>

Public Member Functions
	KalmanFilter ()
	Constructor. More...
virtual	~KalmanFilter ()
	Destructor. More...
void	setInitialState (const Eigen::Ref< const Eigen::Matrix< double, M, 1 >> &x)
	Set the initial state vector, x(0), length m. More...
void	setInitialStateCovariance (const Eigen::Ref< const Eigen::Matrix< double, M, M >> &p)
	Set the initial covariance of the state, P(0), size m x m. More...
void	setStateTransition (const Eigen::Ref< const Eigen::Matrix< double, M, M >> &a)
	Set the state transition, A, such that x(t+1) = A.x(t), size m x m. More...
void	setObservationMatrix (const Eigen::Ref< const Eigen::Matrix< double, N, M >> &h)
	Set the observation matrix, H, such that z(t) = H.x(t), size n x m. More...
void	setProcessNoiseCovariance (const Eigen::Ref< const Eigen::Matrix< double, M, M >> &q)
	Set the process noise covariance, size m x m. More...
void	setMeasurementNoiseCovariance (const Eigen::Ref< const Eigen::Matrix< double, N, N >> &r)
	Set the measurement noise covariance, size n x n. More...
const Eigen::Matrix< double, M, 1 > &	update ()
	Advance one step without a measurement. More...
const Eigen::Matrix< double, M, 1 > &	update (const Eigen::Ref< const Eigen::Matrix< double, N, 1 >> &measurement)
	Advance one step with measurement. More...
const Eigen::Matrix< double, M, 1 > &	getState () const
	Get the current state. More...

Private Member Functions
void	updatePrediction ()
	Use the current estimated state, x(t), and matrices to predict the new state, x(t+1), and state covariance, P(t+1). More...
void	updateMeasurement (const Eigen::Ref< const Eigen::Matrix< double, N, 1 >> &measurement)
	Correct the current estimated state, x(t), and state covariance, P(t), based on a measurement, z(t). More...

Private Attributes
Eigen::Matrix< double, M, M, Eigen::RowMajor >	m_stateTransition
	The state transition matrix. More...
Eigen::Matrix< double, N, M, Eigen::RowMajor >	m_observationMatrix
	The observation matrix. More...
Eigen::Matrix< double, M, M, Eigen::RowMajor >	m_processNoiseCovariance
	The process noise covariance. More...
Eigen::Matrix< double, N, N, Eigen::RowMajor >	m_measurementNoiseCovariance
	The measurement noise covariance. More...
Eigen::Matrix< double, M, 1 >	m_state
	The state. More...
Eigen::Matrix< double, M, M, Eigen::RowMajor >	m_stateCovariance
	The covariance of the state. More...

Detailed Description

template<size_t M, size_t N>
class SurgSim::Math::KalmanFilter< M, N >

Implements a linear Kalman filter, a recursive estimator.

Does not support control inputs.

Template Parameters

M	The length of the state vector.
N	The length of the measurement vector.

Constructor & Destructor Documentation

KalmanFilter()

template<size_t M, size_t N>

SurgSim::Math::KalmanFilter< M, N >::KalmanFilter

(

)

Constructor.

~KalmanFilter()

template<size_t M, size_t N>

SurgSim::Math::KalmanFilter< M, N >::~KalmanFilter ( )

virtual

Destructor.

Member Function Documentation

getState()

template<size_t M, size_t N>

const Eigen::Matrix< double, M, 1 > & SurgSim::Math::KalmanFilter< M, N >::getState

(

)

const

Get the current state.

Does not advance the state.

Returns

The estimate for the current state, x(t), length m.

setInitialState()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setInitialState

(

const Eigen::Ref< const Eigen::Matrix< double, M, 1 >> &

x

)

Set the initial state vector, x(0), length m.

Parameters

x	The initial state.

setInitialStateCovariance()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setInitialStateCovariance

(

const Eigen::Ref< const Eigen::Matrix< double, M, M >> &

p

)

Set the initial covariance of the state, P(0), size m x m.

Parameters

p	The initial covariance.

setMeasurementNoiseCovariance()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setMeasurementNoiseCovariance

(

const Eigen::Ref< const Eigen::Matrix< double, N, N >> &

r

)

Set the measurement noise covariance, size n x n.

Parameters

r	The measurement noise covariance.

setObservationMatrix()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setObservationMatrix

(

const Eigen::Ref< const Eigen::Matrix< double, N, M >> &

h

)

Set the observation matrix, H, such that z(t) = H.x(t), size n x m.

Parameters

h	The observation matrix.

setProcessNoiseCovariance()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setProcessNoiseCovariance

(

const Eigen::Ref< const Eigen::Matrix< double, M, M >> &

q

)

Set the process noise covariance, size m x m.

Parameters

q	The process noise covariance.

setStateTransition()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::setStateTransition

(

const Eigen::Ref< const Eigen::Matrix< double, M, M >> &

a

)

Set the state transition, A, such that x(t+1) = A.x(t), size m x m.

Parameters

a	The state transition matrix.

update() [1/2]

template<size_t M, size_t N>

const Eigen::Matrix< double, M, 1 > & SurgSim::Math::KalmanFilter< M, N >::update

(

)

Advance one step without a measurement.

Returns

The estimate for the new state, x(t+1), length m.

update() [2/2]

template<size_t M, size_t N>

const Eigen::Matrix< double, M, 1 > & SurgSim::Math::KalmanFilter< M, N >::update

(

const Eigen::Ref< const Eigen::Matrix< double, N, 1 >> &

measurement

)

Advance one step with measurement.

Parameters

measurement

The measurement, z(t), length n.

Returns

The estimate for the new state, x(t+1), length m.

updateMeasurement()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::updateMeasurement ( const Eigen::Ref< const Eigen::Matrix< double, N, 1 >> &  measurement )

private

Correct the current estimated state, x(t), and state covariance, P(t), based on a measurement, z(t).

Parameters

measurement

The measurement, length n.

updatePrediction()

template<size_t M, size_t N>

void SurgSim::Math::KalmanFilter< M, N >::updatePrediction ( )

private

Use the current estimated state, x(t), and matrices to predict the new state, x(t+1), and state covariance, P(t+1).

Member Data Documentation

m_measurementNoiseCovariance

template<size_t M, size_t N>

Eigen::Matrix<double, N, N, Eigen::RowMajor> SurgSim::Math::KalmanFilter< M, N >::m_measurementNoiseCovariance

private

The measurement noise covariance.

m_observationMatrix

template<size_t M, size_t N>

Eigen::Matrix<double, N, M, Eigen::RowMajor> SurgSim::Math::KalmanFilter< M, N >::m_observationMatrix

private

The observation matrix.

m_processNoiseCovariance

template<size_t M, size_t N>

Eigen::Matrix<double, M, M, Eigen::RowMajor> SurgSim::Math::KalmanFilter< M, N >::m_processNoiseCovariance

private

The process noise covariance.

m_state

template<size_t M, size_t N>

Eigen::Matrix<double, M, 1> SurgSim::Math::KalmanFilter< M, N >::m_state

private

The state.

m_stateCovariance

template<size_t M, size_t N>

Eigen::Matrix<double, M, M, Eigen::RowMajor> SurgSim::Math::KalmanFilter< M, N >::m_stateCovariance

private

The covariance of the state.

m_stateTransition

template<size_t M, size_t N>

Eigen::Matrix<double, M, M, Eigen::RowMajor> SurgSim::Math::KalmanFilter< M, N >::m_stateTransition

private

The state transition matrix.

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

• SurgSim/Math/KalmanFilter.h
• SurgSim/Math/KalmanFilter-inl.h