This abstract class defines common interfaces and types for a generic Kalman filter. More...

#include <kalman_filter.h>

Inheritance diagram for mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >:

Classes
struct	statecov_t
	Helper struct for passing around the state and its covariance in one variable. More...

Public Types
typedef Eigen::Matrix< double, n, 1 >	x_t
	State vector type $n \times 1$ .

typedef Eigen::Matrix< double, m, 1 >	u_t
	Input vector type $m \times 1$ .

typedef Eigen::Matrix< double, p, 1 >	z_t
	Measurement vector type $p \times 1$ .

typedef Eigen::Matrix< double, n, n >	P_t
	State uncertainty covariance matrix type $n \times n$ .

typedef Eigen::Matrix< double, p, p >	R_t
	Measurement noise covariance matrix type $p \times p$ .

typedef Eigen::Matrix< double, n, n >	Q_t
	Process noise covariance matrix type $n \times n$ .

Public Member Functions
virtual statecov_t	correct (const statecov_t &sc, const z_t &z, const R_t &R) const =0
	Applies the correction (update, measurement, data) step of the Kalman filter.

virtual statecov_t	predict (const statecov_t &sc, const u_t &u, const Q_t &Q, double dt) const =0
	Applies the prediction (time) step of the Kalman filter.

Static Public Attributes
static const int	n = n_states
	Length of the state vector of the system.

static const int	m = n_inputs
	Length of the input vector of the system.

static const int	p = n_measurements
	Length of the measurement vector of the system.

Detailed Description

template<int n_states, int n_inputs, int n_measurements>
class mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >

This abstract class defines common interfaces and types for a generic Kalman filter.

Template Parameters

n_states	number of states of the system (length of the $\mathbf{x}$ vector).
n_inputs	number of inputs of the system (length of the $\mathbf{u}$ vector).
n_measurements	number of measurements of the system (length of the $\mathbf{z}$ vector).

Member Function Documentation

◆ correct()

template<int n_states, int n_inputs, int n_measurements>

virtual statecov_t mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >::correct	(	const statecov_t &	sc,
		const z_t &	z,
		const R_t &	R
	)		const

pure virtual

Applies the correction (update, measurement, data) step of the Kalman filter.

This method applies the correction step to the state and covariance passed in sc using the measurement z and measurement noise R. An optional parameter param may be used by some implementations, but it is usually ignored. The updated state and covariance after the correction step is returned.

Parameters

sc	The state and covariance to which the correction step is to be applied.
z	The measurement vector to be used for correction.
R	The measurement noise covariance matrix to be used for correction.

Returns: The state and covariance after the correction update.

Implemented in mrs_lib::LKF< n_states, n_inputs, n_measurements >, mrs_lib::LKF< 3, 1, 1 >, mrs_lib::LKF< n_states, n_inputs, -1 >, and mrs_lib::UKF< n_states, n_inputs, n_measurements >.

◆ predict()

template<int n_states, int n_inputs, int n_measurements>

virtual statecov_t mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >::predict	(	const statecov_t &	sc,
		const u_t &	u,
		const Q_t &	Q,
		double	dt
	)		const

pure virtual

Applies the prediction (time) step of the Kalman filter.

This method applies the prediction step to the state and covariance passed in sc using the input u and process noise Q. The state and covariance are updated by dt into the future, if applicable to the implementation. An optional parameter param may be used by some implementations, but it is usually ignored. The updated state and covariance after the prediction step is returned.

Parameters

sc	The state and covariance to which the prediction step is to be applied.
u	The input vector to be used for prediction.
Q	The process noise covariance matrix to be used for prediction.
dt	The time step for the prediction update (the state and covariance will be predicted by dt into the future).

Returns: The state and covariance after the prediction step.

Implemented in mrs_lib::LKF< n_states, n_inputs, n_measurements >, mrs_lib::LKF< 3, 1, 1 >, mrs_lib::LKF< n_states, n_inputs, -1 >, and mrs_lib::UKF< n_states, n_inputs, n_measurements >.

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

include/mrs_lib/kalman_filter.h

Classes

Public Types

Public Member Functions

Static Public Attributes

Detailed Description

Member Function Documentation

◆ correct()

◆ predict()