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

Collaboration diagram for mrs_lib::varstepLKF< n_states, n_inputs, n_measurements >:

Public Types
using	Base_class = LKF< n, m, p >

using	x_t = typename Base_class::x_t

using	u_t = typename Base_class::u_t

using	z_t = typename Base_class::z_t

using	P_t = typename Base_class::P_t

using	R_t = typename Base_class::R_t

using	statecov_t = typename Base_class::statecov_t

using	A_t = typename Base_class::A_t

using	B_t = typename Base_class::B_t

using	H_t = typename Base_class::H_t

using	Q_t = typename Base_class::Q_t

using	generateA_t = std::function< A_t(double)>

using	generateB_t = std::function< B_t(double)>

Public Types inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
using	Base_class = KalmanFilter< n, m, p >
	Base class of this class.

using	x_t = typename Base_class::x_t
	State vector type $n \times 1$ .

using	u_t = typename Base_class::u_t
	Input vector type $m \times 1$ .

using	z_t = typename Base_class::z_t
	Measurement vector type $p \times 1$ .

using	P_t = typename Base_class::P_t
	State uncertainty covariance matrix type $n \times n$ .

using	R_t = typename Base_class::R_t
	Measurement noise covariance matrix type $p \times p$ .

using	Q_t = typename Base_class::Q_t
	Process noise covariance matrix type $n \times n$ .

using	statecov_t = typename Base_class::statecov_t
	Helper struct for passing around the state and its covariance in one variable.

typedef Eigen::Matrix< double, n, n >	A_t
	System transition matrix type $n \times n$ .

typedef Eigen::Matrix< double, n, m >	B_t
	Input to state mapping matrix type $n \times m$ .

typedef Eigen::Matrix< double, p, n >	H_t
	State to measurement mapping matrix type $p \times n$ .

typedef Eigen::Matrix< double, n, p >	K_t
	Kalman gain matrix type $n \times p$ .

Public Types inherited from mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >
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
	varstepLKF (const generateA_t &generateA, const generateB_t &generateB, const H_t &H)
	The main constructor.

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

Public Member Functions inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
	LKF ()
	Convenience default constructor.

	LKF (const A_t &A, const B_t &B, const H_t &H)
	The main constructor.

virtual statecov_t	correct (const statecov_t &sc, const z_t &z, const R_t &R) const override
	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 override
	Applies the prediction (time) step of the Kalman filter.

Static Public Attributes
static const int	n = n_states

static const int	m = n_inputs

static const int	p = n_measurements

Static Public Attributes inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
static constexpr int	n = n_states
	Length of the state vector of the system.

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

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

Static Public Attributes inherited from mrs_lib::KalmanFilter< n_states, n_inputs, n_measurements >
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.

Additional Inherited Members
Public Attributes inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
A_t	A
	The system transition matrix $n \times n$ .

B_t	B
	The input to state mapping matrix $n \times m$ .

H_t	H
	The state to measurement mapping matrix $p \times n$ .

Protected Member Functions inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
virtual K_t	computeKalmanGain (const statecov_t &sc, const z_t &z, const R_t &R, const H_t &H) const

statecov_t ::type	correction_impl (const statecov_t &sc, const z_t &z, const R_t &R, const H_t &H) const

Static Protected Member Functions inherited from mrs_lib::LKF< n_states, n_inputs, n_measurements >
static P_t	covariance_predict (const A_t &A, const P_t &P, const Q_t &Q, const double dt)

template<int check = n_inputs>
static std::enable_if< check==0, x_t >::type	state_predict (const A_t &A, const x_t &x, const B_t &B, const u_t &u)

template<int check = n_inputs>
static std::enable_if< check!=0, x_t >::type	state_predict (const A_t &A, const x_t &x, const B_t &B, const u_t &u)

static R_t	invert_W (R_t W)

Constructor & Destructor Documentation

◆ varstepLKF()

template<int n_states, int n_inputs, int n_measurements>

mrs_lib::varstepLKF< n_states, n_inputs, n_measurements >::varstepLKF	(	const generateA_t &	generateA,
		const generateB_t &	generateB,
		const H_t &	H
	)

inline

The main constructor.

Parameters

generateA	a function, which returns the state transition matrix `A` based on the time difference `dt`.
generateB	a function, which returns the input to state mapping matrix `B` based on the time difference `dt`.
H	the state to measurement mapping matrix.

Member Function Documentation

◆ predict()

template<int n_states, int n_inputs, int n_measurements>

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

inlineoverridevirtual

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

This method applies the linear Kalman filter prediction step to the state and covariance passed in sc using the input u and process noise Q. The process noise covariance Q is scaled by the dt parameter. 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	Used to scale the process noise covariance `Q` and to generate the state transition and input to state mapping matrices `A` and \B using the functions, passed in the object's constructor.

Returns: The state and covariance after the prediction step.

Note: Note that the dt parameter is used to scale the process noise covariance Q and to generate the system matrices A or B using the functions, passed in the constructor!

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

include/mrs_lib/lkf.h

Public Types

Public Member Functions

Static Public Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ varstepLKF()

Member Function Documentation

◆ predict()