rls_filter::RLSFilter< T, N > Class Template Reference

#include <RLSFilter.h>

Public Types
using	VectorXt = Matrix<T, N, 1>
using	MatrixXt = Matrix<T, N, N>

Public Member Functions
template<int N1 = N, EnableIfB<(N1 > 0)> = 0>
	RLSFilter (T lam, T delta)
template<int N1 = N, EnableIfB<(N1==-1)> = 0>
	RLSFilter (unsigned int n, T lam, T delta)
void	update (const VectorXt &x, T y)
T	predict (const VectorXt &x) const noexcept
void	setEstimatedCoefficients (const VectorXt &w0)
void	setForgettingFactor (double lam)
void	setInitialCovarianceMatrixGain (double delta)
const VectorXt &	estimatedCoefficients () const noexcept
T	a_priori_err () const noexcept
const VectorXt &	gains () const noexcept
const MatrixXt &	P () const noexcept
unsigned long long	count () const noexcept
void	reset () noexcept
	Reset filter to initial values.

Private Attributes
unsigned int	n_
T	lam_
T	lam_inv_
T	delta_
VectorXt	w_
MatrixXt	P_
MatrixXt	P_supp_
VectorXt	g_
T	err_
unsigned long long	count_

Detailed Description

template<typename T, int N>
class rls_filter::RLSFilter< T, N >

Template class implementing a Recursive Least Square (RLS) filter, managing both static and dynamic implementation.

Template Parameters

T	filter data values type
N	filter order(Static) or -1 (Dynamic)

Member Typedef Documentation

MatrixXt

template<typename T, int N>

using rls_filter::RLSFilter< T, N >::MatrixXt = Matrix<T, N, N>

VectorXt

template<typename T, int N>

using rls_filter::RLSFilter< T, N >::VectorXt = Matrix<T, N, 1>

Constructor & Destructor Documentation

RLSFilter() [1/2]

template<typename T, int N>

template<int N1 = N, EnableIfB<(N1 > 0)> = 0>

rls_filter::RLSFilter< T, N >::RLSFilter ( T lam, T delta )

inline

Recursive least square filter static ctor

Parameters

lam	- Forgetting factor
delta	- Initial gain value of matrix P

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RLSFilter() [2/2]

template<typename T, int N>

template<int N1 = N, EnableIfB<(N1==-1)> = 0>

rls_filter::RLSFilter< T, N >::RLSFilter ( unsigned int n, T lam, T delta )

inline

Recursive least square filter dynamic ctor

Parameters

n	- Filter order
lam	- Forgetting factor
delta	- Initial gain value of matrix P

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Member Function Documentation

a_priori_err()

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::a_priori_err ( ) const

inlinenoexcept

Get a priori estimate error

Returns

a priori error

count()

template<typename T, int N>

unsigned long long rls_filter::RLSFilter< T, N >::count ( ) const

inlinenoexcept

Get number of performed updates

Returns

update count

estimatedCoefficients()

template<typename T, int N>

const VectorXt & rls_filter::RLSFilter< T, N >::estimatedCoefficients ( ) const

inlinenoexcept

Get estimated filter coefficients

Returns

vector of estimated filter coefficients

gains()

template<typename T, int N>

const VectorXt & rls_filter::RLSFilter< T, N >::gains ( ) const

inlinenoexcept

Get filter gains vector

Returns

filter gains vector

P()

template<typename T, int N>

const MatrixXt & rls_filter::RLSFilter< T, N >::P ( ) const

inlinenoexcept

Get filter covariance matrix

Returns

filter covariance matrix

predict()

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::predict ( const VectorXt & x ) const

inlinenoexcept

Estimate filter output

Parameters

x

Returns

a priori output estimate

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reset()

template<typename T, int N>

void rls_filter::RLSFilter< T, N >::reset ( )

inlinenoexcept

Reset filter to initial values.

setEstimatedCoefficients()

template<typename T, int N>

void rls_filter::RLSFilter< T, N >::setEstimatedCoefficients ( const VectorXt & w0 )

inline

Set filter coefficient values

Parameters

w0	- Coefficient values

setForgettingFactor()

template<typename T, int N>

void rls_filter::RLSFilter< T, N >::setForgettingFactor ( double lam )

inline

Set forgetting factor value

Parameters

lam	- Forgetting factor value

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setInitialCovarianceMatrixGain()

template<typename T, int N>

void rls_filter::RLSFilter< T, N >::setInitialCovarianceMatrixGain ( double delta )

inline

Set initial covariance matrix gain

Parameters

delta

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update()

template<typename T, int N>

void rls_filter::RLSFilter< T, N >::update ( const VectorXt & x, T y )

inline

Update filter with new data

Parameters

x	- Input vector
y	- Output value

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Member Data Documentation

count_

template<typename T, int N>

unsigned long long rls_filter::RLSFilter< T, N >::count_

private

Count of filter updates

delta_

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::delta_

private

Initial gain value of matrix P

err_

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::err_

private

A priori error

g_

template<typename T, int N>

VectorXt rls_filter::RLSFilter< T, N >::g_

private

Filter gains

lam_

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::lam_

private

Forgetting factor

lam_inv_

template<typename T, int N>

T rls_filter::RLSFilter< T, N >::lam_inv_

private

Inverse forgetting factor

n_

template<typename T, int N>

unsigned int rls_filter::RLSFilter< T, N >::n_

private

Filter order

P_

template<typename T, int N>

MatrixXt rls_filter::RLSFilter< T, N >::P_

private

Inverse covariance error matrix

P_supp_

template<typename T, int N>

MatrixXt rls_filter::RLSFilter< T, N >::P_supp_

private

Inverse covariance error matrix

w_

template<typename T, int N>

VectorXt rls_filter::RLSFilter< T, N >::w_

private

Filter coefficients vector

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

• include/RLSFilter.h