EKF_Filter Class Reference

Extended Kalman Filter (EKF) class for state estimation. More...

#include <extended-kalman.h>

Public Member Functions
	EKF_Filter ()
	Default constructor for the EKF_Filter class. Initializes the EKF and reads the Anchor positions from EEPROM. More...
	~EKF_Filter ()
	Destructor of Kalman-Filter Object, cleanup recources. More...
Matrix< double, DIM_X, 1 > &	vecX ()
	Get the estimated state vector. More...
const Matrix< double, DIM_X, 1 > &	vecX () const
	Get the estimated state vector (const version). More...
Matrix< double, DIM_X, DIM_X > &	matP ()
	Get the state covariance matrix. More...
const Matrix< double, DIM_X, DIM_X > &	matP () const
	Get the state covariance matrix (const version). More...
void	predict (const Matrix< double, DIM_X, DIM_X > &matF, const Matrix< double, DIM_X, DIM_X > &matQ)
	Perform the prediction step of the EKF. More...
void	correct (const Matrix< double, DIM_Z, 1 > &vecZ, const Matrix< double, DIM_Z, DIM_Z > &matR, const Matrix< double, DIM_Z, DIM_X > &matH)
	Perform the correction step of the EKF. More...
template<typename PredictionModelCallback >
void	predictEkf (PredictionModelCallback predictionModel, const Matrix< double, DIM_X, DIM_X > &matJacobF, const Matrix< double, DIM_X, DIM_X > &matQ)
	Perform the prediction step of the EKF using a user-defined prediction model. More...
template<typename MeasurementModelCallback >
void	correctEkf (MeasurementModelCallback measurementModel, const Matrix< double, DIM_Z, 1 > &vecZ, const Matrix< double, DIM_Z, DIM_Z > &matR, const Matrix< double, DIM_Z, DIM_X > &matJcobH)
	Perform the correction step of the EKF using a user-defined measurement model. More...

Private Member Functions
void	read_landmarks_from_eeprom (void)
	state covariance matrix More...

Private Attributes
Matrix< double, DIM_X, 1 >	m_vecX { Matrix<double, DIM_X, 1>::Zero() }
Matrix< double, DIM_X, DIM_X >	m_matP { Matrix<double, DIM_X, DIM_X>::Zero() }
	estimated state vector More...

Detailed Description

Extended Kalman Filter (EKF) class for state estimation.

Constructor & Destructor Documentation

EKF_Filter()

EKF_Filter

(

)

Default constructor for the EKF_Filter class. Initializes the EKF and reads the Anchor positions from EEPROM.

~EKF_Filter()

~EKF_Filter ( )

inline

Destructor of Kalman-Filter Object, cleanup recources.

Member Function Documentation

correct()

void correct ( const Matrix< double, DIM_Z, 1 > &  vecZ, const Matrix< double, DIM_Z, DIM_Z > &  matR, const Matrix< double, DIM_Z, DIM_X > &  matH  )

inline

Perform the correction step of the EKF.

Parameters

vecZ	The measurement vector.
matR	The measurement noise covariance matrix.
matH	The measurement Jacobian matrix.

correctEkf()

void correctEkf ( MeasurementModelCallback  measurementModel, const Matrix< double, DIM_Z, 1 > &  vecZ, const Matrix< double, DIM_Z, DIM_Z > &  matR, const Matrix< double, DIM_Z, DIM_X > &  matJcobH  )

inline

Perform the correction step of the EKF using a user-defined measurement model.

Template Parameters

MeasurementModelCallback

The type of the measurement model callback function.

Parameters

measurementModel	The measurement model callback function.
vecZ	The measurement vector.
matR	The measurement noise covariance matrix.
matJcobH	The measurement Jacobian matrix.

matP() [1/2]

Matrix< double, DIM_X, DIM_X > & matP ( )

inline

Get the state covariance matrix.

Returns

Reference to the state covariance matrix.

matP() [2/2]

const Matrix< double, DIM_X, DIM_X > & matP ( ) const

inline

Get the state covariance matrix (const version).

Returns

Const reference to the state covariance matrix.

predict()

void predict ( const Matrix< double, DIM_X, DIM_X > &  matF, const Matrix< double, DIM_X, DIM_X > &  matQ  )

inline

Perform the prediction step of the EKF.

Parameters

matF	The state transition matrix.
matQ	The process noise covariance matrix.

predictEkf()

void predictEkf ( PredictionModelCallback  predictionModel, const Matrix< double, DIM_X, DIM_X > &  matJacobF, const Matrix< double, DIM_X, DIM_X > &  matQ  )

inline

Perform the prediction step of the EKF using a user-defined prediction model.

Template Parameters

PredictionModelCallback

The type of the prediction model callback function.

Parameters

predictionModel	The prediction model callback function.
matJacobF	The Jacobian matrix of the prediction model.
matQ	The process noise covariance matrix.

read_landmarks_from_eeprom()

void read_landmarks_from_eeprom ( void  )

private

state covariance matrix

Reads landmarks from EEPROM and stores them in the landmarkPositions matrix.

vecX() [1/2]

Matrix< double, DIM_X, 1 > & vecX ( )

inline

Get the estimated state vector.

Returns

Reference to the estimated state vector.

vecX() [2/2]

const Matrix< double, DIM_X, 1 > & vecX ( ) const

inline

Get the estimated state vector (const version).

Returns

Const reference to the estimated state vector.

Member Data Documentation

m_matP

Matrix<double, DIM_X, DIM_X> m_matP { Matrix<double, DIM_X, DIM_X>::Zero() }

private

estimated state vector

m_vecX

Matrix<double, DIM_X, 1> m_vecX { Matrix<double, DIM_X, 1>::Zero() }

private

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

• lib/common/extended-kalman.h
• lib/common/extended-kalman.cpp