kalman_filter< state_d, measure_d, control_d > Class Template Reference

Template-based Kalman filter implementation. More...

#include <kalman.h>

Protected Member Functions
	kalman_filter ()
void	estimate ()
void	correct ()

Protected Attributes
mtx_t< double, state_d, state_d >	F
mtx_t< double, measure_d, state_d >	H
	state transition model
mtx_t< double, state_d, control_d >	B
	observation model
mtx_t< double, state_d, state_d >	Q
	control matrix
mtx_t< double, measure_d, measure_d >	R
	process noise covariance
mtx_t< double, control_d, 1 >	Uk
	observation noise covariance
mtx_t< double, measure_d, 1 >	Zk
	control vector
mtx_t< double, state_d, 1 >	Xk_km1
	actual measured values vector
mtx_t< double, state_d, state_d >	Pk_km1
	predicted state estimate
mtx_t< double, measure_d, 1 >	Yk
	predicted estimate covariance
mtx_t< double, measure_d, measure_d >	Sk
	measurement innovation
mtx_t< double, state_d, measure_d >	K
	innovation covariance
mtx_t< double, state_d, 1 >	Xk_k
	Kalman gain.
mtx_t< double, measure_d, 1 >	Yk_k
	updated (current) state
mtx_t< double, state_d, state_d >	Pk_k
	post fit residual
mtx_t< double, state_d, state_d >	I
	updated estimate covariance

Detailed Description

template<size_t state_d, size_t measure_d, size_t control_d>
class kalman_filter< state_d, measure_d, control_d >

Template-based Kalman filter implementation.

Template Parameters

state_d	Dimension of state vector (number of state variables)
measure_d	Dimension of measurement vector (number of measurements)
control_d	Dimension of control vector (number of control inputs)

Generic Kalman filter for linear state estimation using Eigen matrices. Implements standard predict-correct cycle with configurable dimensions.

Constructor & Destructor Documentation

kalman_filter()

template<size_t state_d, size_t measure_d, size_t control_d>

kalman_filter< state_d, measure_d, control_d >::kalman_filter ( )

inlineprotected

Member Function Documentation

correct()

template<size_t state_d, size_t measure_d, size_t control_d>

void kalman_filter< state_d, measure_d, control_d >::correct ( )

inlineprotected

estimate()

template<size_t state_d, size_t measure_d, size_t control_d>

void kalman_filter< state_d, measure_d, control_d >::estimate ( )

inlineprotected

Member Data Documentation

B

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, control_d> kalman_filter< state_d, measure_d, control_d >::B

protected

observation model

F

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, state_d> kalman_filter< state_d, measure_d, control_d >::F

protected

H

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, state_d> kalman_filter< state_d, measure_d, control_d >::H

protected

state transition model

I

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, state_d> kalman_filter< state_d, measure_d, control_d >::I

protected

updated estimate covariance

K

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, measure_d> kalman_filter< state_d, measure_d, control_d >::K

protected

innovation covariance

Pk_k

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, state_d> kalman_filter< state_d, measure_d, control_d >::Pk_k

protected

post fit residual

Pk_km1

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, state_d> kalman_filter< state_d, measure_d, control_d >::Pk_km1

protected

predicted state estimate

Q

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, state_d> kalman_filter< state_d, measure_d, control_d >::Q

protected

control matrix

R

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, measure_d> kalman_filter< state_d, measure_d, control_d >::R

protected

process noise covariance

Sk

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, measure_d> kalman_filter< state_d, measure_d, control_d >::Sk

protected

measurement innovation

Uk

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, control_d, 1> kalman_filter< state_d, measure_d, control_d >::Uk

protected

observation noise covariance

Xk_k

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, 1> kalman_filter< state_d, measure_d, control_d >::Xk_k

protected

Kalman gain.

Xk_km1

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, state_d, 1> kalman_filter< state_d, measure_d, control_d >::Xk_km1

protected

actual measured values vector

Yk

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, 1> kalman_filter< state_d, measure_d, control_d >::Yk

protected

predicted estimate covariance

Yk_k

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, 1> kalman_filter< state_d, measure_d, control_d >::Yk_k

protected

updated (current) state

Zk

template<size_t state_d, size_t measure_d, size_t control_d>

mtx_t<double, measure_d, 1> kalman_filter< state_d, measure_d, control_d >::Zk

protected

control vector

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

• inc/kalman.h