Generic Linear Kalman Filter template base class. More...

#include <kfilter.hpp>

Inheritance diagram for Kalman::KFilter< T, BEG, OQ, OVR, DBG >:

Public Member Functions
virtual	~KFilter ()=0
	Virtual destructor. More...

Public Member Functions inherited from Kalman::EKFilter< T, BEG, OQ, OVR, DBG >
void	init (Vector &x_, Matrix &P_)
	Sets initial conditions for the Kalman Filter. More...

	EKFilter ()
	Default constructor. More...

	EKFilter (K_UINT_32 n_, K_UINT_32 nu_, K_UINT_32 nw_, K_UINT_32 m_, K_UINT_32 nv_)
	Constructors specifying all necessary matrix and vector dimensions. More...

virtual	~EKFilter ()
	Virtual destructor. More...

K_UINT_32	getSizeX () const
	Returns the size of the state vector. More...

K_UINT_32	getSizeU () const
	Returns the size of the input vector. More...

K_UINT_32	getSizeW () const
	Returns the size of the process noise vector. More...

K_UINT_32	getSizeZ () const
	Returns the size of the measurement vector. More...

K_UINT_32	getSizeV () const
	Returns the size of the measurement noise vector. More...

void	setDim (K_UINT_32 n_, K_UINT_32 nu_, K_UINT_32 nw_, K_UINT_32 m_, K_UINT_32 nv_)
	Sets all dimensions at once. More...

void	setSizeX (K_UINT_32 n_)
	Sets the size of the state vector. More...

void	setSizeU (K_UINT_32 nu_)
	Sets the size of the input vector. More...

void	setSizeW (K_UINT_32 nw_)
	Sets the size of the process noise vector. More...

void	setSizeZ (K_UINT_32 m_)
	Sets the size of the measurement vector. More...

void	setSizeV (K_UINT_32 nv_)
	Sets the size of the measurement noise vector. More...

void	step (Vector &u_, const Vector &z_)
	Makes one prediction-correction step. More...

void	timeUpdateStep (Vector &u_)
	Makes one prediction step. More...

void	measureUpdateStep (const Vector &z_)
	Makes one correction step. More...

const Vector &	predict (Vector &u_)
	Returns the predicted state vector (a priori state estimate). More...

const Vector &	simulate ()
	Returns the predicted measurement vector. More...

const Vector &	getX () const
	Returns the corrected state (a posteriori state estimate). More...

const Matrix &	calculateP () const
	Returns the a posteriori error covariance estimate matrix. More...

Protected Member Functions
virtual void	makeB ()
	Virtual creator of B. More...

virtual void	makeBaseB ()
	Virtual pre-creator of B. More...

Protected Member Functions inherited from Kalman::EKFilter< T, BEG, OQ, OVR, DBG >
void	NoModification ()
	Allows optimizations on some calculations. More...

virtual void	makeBaseA ()
	Virtual pre-creator of A. More...

virtual void	makeBaseW ()
	Virtual pre-creator of W. More...

virtual void	makeBaseQ ()
	Virtual pre-creator of Q. More...

virtual void	makeBaseH ()
	Virtual pre-creator of H. More...

virtual void	makeBaseV ()
	Virtual pre-creator of V. More...

virtual void	makeBaseR ()
	Virtual pre-creator of R. More...

virtual void	makeCommonProcess ()
	Optional function used to precalculate common values for process. More...

virtual void	makeA ()
	Virtual creator of A. More...

virtual void	makeW ()
	Virtual creator of W. More...

virtual void	makeQ ()
	Virtual creator of Q. More...

virtual void	makeCommonMeasure ()
	Optional function used to precalculate common values for measurement. More...

virtual void	makeH ()
	Virtual creator of H. More...

virtual void	makeV ()
	Virtual creator of V. More...

virtual void	makeR ()
	Virtual creator of R. More...

virtual void	makeDZ ()
	Hook-up function to modify innovation vector. More...

Protected Attributes
Matrix	B
	Input matrix. More...

Protected Attributes inherited from Kalman::EKFilter< T, BEG, OQ, OVR, DBG >
Vector	x
	Corrected state vector. More...

Vector	u
	Input vector. More...

Vector	z
	Predicted measurement vector. More...

Vector	dz
	Innovation vector. More...

Matrix	A
	A jacobian matrix. More...

Matrix	W
	A jacobian matrix. More...

Matrix	Q
	Process noise covariance matrix. More...

Matrix	H
	A jacobian matrix. More...

Matrix	V
	A jacobian matrix. More...

Matrix	R
	Measurement noise covariance matrix. More...

K_UINT_32	n
	Size of the state vector. More...

K_UINT_32	nu
	Size of the input vector. More...

K_UINT_32	nw
	Size of the process noise vector. More...

K_UINT_32	m
	Size of the measurement vector. More...

K_UINT_32	nv
	Size of the measurement noise vector. More...

Private Member Functions
virtual void	makeMeasure ()
	Measurement function overridden to be linear. More...

virtual void	makeProcess ()
	Process function overridden to be linear. More...

virtual void	sizeUpdate ()
	Matrix and vector resizing function, overridden to take B into account. More...

Private Attributes
Vector	x__
	Temporary vector. More...

Additional Inherited Members
Public Types inherited from Kalman::EKFilter< T, BEG, OQ, OVR, DBG >
enum	{ beg = BEG }

typedef KMatrix< T, BEG, DBG >	Matrix
	Matrix type. More...

typedef T	type
	Type of objects contained in matrices and vectors. More...

typedef KVector< T, BEG, DBG >	Vector
	Vector type. More...

Detailed Description

template<typename T, K_UINT_32 BEG, bool OQ = false, bool OVR = false, bool DBG = true>
class Kalman::KFilter< T, BEG, OQ, OVR, DBG >

Generic Linear Kalman Filter template base class.

Usage: This class is derived from EKFilter. You should really read all the documentation of EKFilter before reading this.
This class implements a Variable-Dimension Linear Kalman Filter based on the EKFilter template class.

Notation

Assume a state vector $ x $

(to estimate) and a linear process function $ f $

(to model) that describes the evolution of this state through time, that is :

$x_k = f \left( x_{k-1}, u_{k-1}, w_{k-1} \right) = //! A x_{k-1} + B u_{k-1} + W w_{k-1}$

where

is the (known) input vector fed to the process and $ w $

is the (unknown) process noise vector due to uncertainty and process modeling errors. Further suppose that the (known) process noise covariance matrix is :

$Q = E \left( w w^T \right)$

Now, let's assume a (known) measurement vector $ z $

, which depends on the current state $ x $

in the form of a linear function $ h $

(to model) :

$z_k = h \left( x_k, v_k \right) = //! H x_k + V v_k$

where

is the (unknown) measurement noise vector with a (known) covariance matrix :

$R = E \left( v v^T \right)$

Suppose that we have an estimate of the previous state $\hat{x}_{k-1}$ , called a corrected state or an a posteriori state estimate. We can build a predicted state (also called an a priori state estimate) by using $ f $

:

$\tilde{x}_k = f \left( \hat{x}_{k-1}, u_{k-1}, 0 \right) = //! A \hat{x}_{k-1} + B u_{k-1}$

since the input is known and the process noise, unknown. With this predicted state, we can get a predicted measurement vector by using $ h $

:

$\tilde{z}_k = h \left( \tilde{x}_k, 0 \right) = //! H \tilde{x}_k$

since the measurement noise is unknown.

Note: In this class, makeProcess() and makeMeasure() have already been overridden, and cannot be ovverridden again. However, there is a new matrix to create : B. This means there are two new virtual functions that can be overridden : makeBaseB() and makeB().

See Also: EKFilter

Definition at line 83 of file kfilter.hpp.