Unscented Kalman Filter is a nonlinear estimator best suited for highly nonlinear systems. It combines the principles of EKF and particle filter. The downside is the computation complexity. More...

#include <UnscentedKalmanFilter.h>

Inheritance diagram for ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >:

Public Types
using	Base = EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >

template<size_t SIZE>
using	SigmaPoints = Eigen::Matrix< SCALAR, SIZE, SigmaPointCount >

template<size_t SIZE>
using	Covariance = Eigen::Matrix< SCALAR, SIZE, SIZE >

template<size_t SIZE>
using	CovarianceSquareRoot = Cholesky< Eigen::Matrix< SCALAR, SIZE, SIZE > >

using	control_vector_t = ct::core::ControlVector< CONTROL_DIM, SCALAR >

using	output_matrix_t = ct::core::OutputMatrix< OUTPUT_DIM, SCALAR >

using	output_vector_t = ct::core::OutputVector< OUTPUT_DIM, SCALAR >

using	state_matrix_t = ct::core::StateMatrix< STATE_DIM, SCALAR >

using	state_vector_t = ct::core::StateVector< STATE_DIM, SCALAR >

Public Types inherited from ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >
using	control_vector_t = ct::core::ControlVector< CONTROL_DIM, SCALAR >

using	state_vector_t = ct::core::StateVector< STATE_DIM, SCALAR >

using	state_matrix_t = ct::core::StateMatrix< STATE_DIM, SCALAR >

using	output_vector_t = ct::core::OutputVector< OUTPUT_DIM, SCALAR >

using	output_matrix_t = ct::core::OutputMatrix< OUTPUT_DIM, SCALAR >

Public Member Functions
	UnscentedKalmanFilter (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f, std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h, const state_vector_t &x0=state_vector_t::Zero(), SCALAR alpha=SCALAR(1.0), SCALAR beta=SCALAR(2.0), SCALAR kappa=SCALAR(0.0), const ct::core::StateMatrix< STATE_DIM, SCALAR > &P0=ct::core::StateMatrix< STATE_DIM, SCALAR >::Identity())
	Constructor. More...

	UnscentedKalmanFilter (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f, std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h, const UnscentedKalmanFilterSettings< STATE_DIM, SCALAR > &ukf_settings)
	Constructor from settings. More...

const state_vector_t &	predict (const control_vector_t &u, const ct::core::Time &dt, const ct::core::Time &t) override
	Estimator predict method. More...

const state_vector_t &	update (const output_vector_t &y, const ct::core::Time &dt, const ct::core::Time &t) override
	Estimator update method. More...

bool	computeSigmaPoints ()
	Compute sigma points from current state and covariance estimates. More...

template<size_t SIZE>
bool	computeCovarianceFromSigmaPoints (const Eigen::Matrix< SCALAR, SIZE, 1 > &mean, const SigmaPoints< SIZE > &sigmaPoints, const Covariance< SIZE > &noiseCov, Covariance< SIZE > &cov)
	Estimate covariance from sigma points and noise covariance. More...

bool	computeKalmanGain (const ct::core::OutputVector< OUTPUT_DIM, SCALAR > &y, const SigmaPoints< OUTPUT_DIM > &sigmaMeasurementPoints, const Covariance< OUTPUT_DIM > &P_yy, Eigen::Matrix< SCALAR, STATE_DIM, OUTPUT_DIM > &K)
	Compute the Kalman Gain using sigma points.. More...

bool	updateStateCovariance (const Eigen::Matrix< SCALAR, STATE_DIM, OUTPUT_DIM > &K, const Covariance< OUTPUT_DIM > &P)
	Compute the Kalman Gain using sigma points.. More...

const state_vector_t	computeStatePrediction (const ct::core::ControlVector< CONTROL_DIM, SCALAR > &u, const ct::core::Time &dt, const ct::core::Time &t=0)
	Update state covariance from Kalman gain and sigma points. More...

ct::core::OutputVector< OUTPUT_DIM, SCALAR >	computeMeasurementPrediction (SigmaPoints< OUTPUT_DIM > &sigmaMeasurementPoints, const ct::core::Time &t=0)
	Predict the next measurement. More...

void	computeWeights ()
	Compute weights of sigma points. More...

void	computeSigmaPointTransition (const ct::core::ControlVector< CONTROL_DIM, SCALAR > &u, const ct::core::Time &dt, const ct::core::Time &t)
	Propagate sigma points through the system dynamics. Using CTSystemModel here will not provide a desired effect since it disregards the control input u. More...

void	computeSigmaPointMeasurements (SigmaPoints< OUTPUT_DIM > &sigmaMeasurementPoints, const ct::core::Time &t=0)
	Predict measurements of sigma points. More...

template<size_t DIM>
state_vector_t	computePredictionFromSigmaPoints (const SigmaPoints< DIM > &sigmaPoints)
	Make a prediction based on sigma points. More...

template<size_t DIM>
auto	computePredictionFromSigmaPoints (const SigmaPoints< DIM > &sigmaPoints) -> state_vector_t

Public Member Functions inherited from ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >
	EstimatorBase (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f, std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h, const state_vector_t &x0=state_vector_t::Zero())
	Constructor. More...

	EstimatorBase (const EstimatorBase &arg)
	Copy constructor. More...

virtual	~EstimatorBase ()=default

void	setSystemModel (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f)
	update the system model More...

void	setMeasurementModel (std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h)
	update the measurement model More...

const state_vector_t &	getEstimate () const
	Estimate getter. More...

void	setEstimate (const state_vector_t &x)
	Estimate setter. More...

Static Public Attributes
static constexpr size_t	SigmaPointCount = 2 * STATE_DIM + 1

Additional Inherited Members
Protected Attributes inherited from ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >
std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR > >	f_
	System model for propagating the system. More...

std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR > >	h_
	Observation model used to calculate the output error. More...

state_vector_t	x_est_
	State estimate. More...

Detailed Description

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>
class ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >

Unscented Kalman Filter is a nonlinear estimator best suited for highly nonlinear systems. It combines the principles of EKF and particle filter. The downside is the computation complexity.

Template Parameters

STATE_DIM

Member Typedef Documentation

◆ Base

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::Base = EstimatorBase<STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR>

◆ SigmaPoints

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

template<size_t SIZE>

using ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::SigmaPoints = Eigen::Matrix<SCALAR, SIZE, SigmaPointCount>

◆ Covariance

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

template<size_t SIZE>

using ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::Covariance = Eigen::Matrix<SCALAR, SIZE, SIZE>

◆ CovarianceSquareRoot

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

template<size_t SIZE>

using ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::CovarianceSquareRoot = Cholesky<Eigen::Matrix<SCALAR, SIZE, SIZE> >

◆ control_vector_t

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::control_vector_t = ct::core::ControlVector<CONTROL_DIM, SCALAR>

◆ output_matrix_t

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::output_matrix_t = ct::core::OutputMatrix<OUTPUT_DIM, SCALAR>

◆ output_vector_t

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::output_vector_t = ct::core::OutputVector<OUTPUT_DIM, SCALAR>

◆ state_matrix_t

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::state_matrix_t = ct::core::StateMatrix<STATE_DIM, SCALAR>

◆ state_vector_t

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

using ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::state_vector_t = ct::core::StateVector<STATE_DIM, SCALAR>

Constructor & Destructor Documentation

◆ UnscentedKalmanFilter() [1/2]

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::UnscentedKalmanFilter	(	std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >>	f,
		std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >>	h,
		const state_vector_t &	x0 = `state_vector_t::Zero()`,
		SCALAR	alpha = `SCALAR(1.0)`,
		SCALAR	beta = `SCALAR(2.0)`,
		SCALAR	kappa = `SCALAR(0.0)`,
		const ct::core::StateMatrix< STATE_DIM, SCALAR > &	P0 = `ct::core::StateMatrix<STATE_DIM, SCALAR>::Identity()`
	)

Constructor.

◆ UnscentedKalmanFilter() [2/2]

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::UnscentedKalmanFilter	(	std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >>	f,
		std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >>	h,
		const UnscentedKalmanFilterSettings< STATE_DIM, SCALAR > &	ukf_settings
	)

Constructor from settings.

Member Function Documentation

◆ predict()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

auto ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::predict	(	const control_vector_t &	u,
		const ct::core::Time &	dt,
		const ct::core::Time &	t
	)

overridevirtual

Estimator predict method.

Implements ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >.

References ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPoints(), ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeStatePrediction(), dt, ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::f_, t, u, and ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::x_est_.

◆ update()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

auto ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::update	(	const output_vector_t &	y,
		const ct::core::Time &	dt,
		const ct::core::Time &	t
	)

overridevirtual

Estimator update method.

Implements ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >.

References ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeKalmanGain(), ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeMeasurementPrediction(), ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::h_, t, ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::updateStateCovariance(), and ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::x_est_.

◆ computeSigmaPoints()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

bool ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPoints ( )

Compute sigma points from current state and covariance estimates.

References ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::x_est_.

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::predict().

◆ computeCovarianceFromSigmaPoints()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

template<size_t SIZE>

bool ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeCovarianceFromSigmaPoints	(	const Eigen::Matrix< SCALAR, SIZE, 1 > &	mean,
		const SigmaPoints< SIZE > &	sigmaPoints,
		const Covariance< SIZE > &	noiseCov,
		Covariance< SIZE > &	cov
	)

Estimate covariance from sigma points and noise covariance.

◆ computeKalmanGain()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

bool ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeKalmanGain	(	const ct::core::OutputVector< OUTPUT_DIM, SCALAR > &	y,
		const SigmaPoints< OUTPUT_DIM > &	sigmaMeasurementPoints,
		const Covariance< OUTPUT_DIM > &	P_yy,
		Eigen::Matrix< SCALAR, STATE_DIM, OUTPUT_DIM > &	K
	)

Compute the Kalman Gain using sigma points..

References ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::x_est_.

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::update().

◆ updateStateCovariance()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

bool ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::updateStateCovariance	(	const Eigen::Matrix< SCALAR, STATE_DIM, OUTPUT_DIM > &	K,
		const Covariance< OUTPUT_DIM > &	P
	)

Compute the Kalman Gain using sigma points..

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::update().

◆ computeStatePrediction()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

auto ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeStatePrediction	(	const ct::core::ControlVector< CONTROL_DIM, SCALAR > &	u,
		const ct::core::Time &	dt,
		const ct::core::Time &	t = `0`
	)

Update state covariance from Kalman gain and sigma points.

References ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointTransition(), dt, t, and u.

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::predict().

◆ computeMeasurementPrediction()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

ct::core::OutputVector< OUTPUT_DIM, SCALAR > ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeMeasurementPrediction	(	SigmaPoints< OUTPUT_DIM > &	sigmaMeasurementPoints,
		const ct::core::Time &	t = `0`
	)

Predict the next measurement.

References ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointMeasurements().

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::update().

◆ computeWeights()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

void ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeWeights ( )

Compute weights of sigma points.

References i, and ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::SigmaPointCount.

◆ computeSigmaPointTransition()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

void ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointTransition	(	const ct::core::ControlVector< CONTROL_DIM, SCALAR > &	u,
		const ct::core::Time &	dt,
		const ct::core::Time &	t
	)

Propagate sigma points through the system dynamics. Using CTSystemModel here will not provide a desired effect since it disregards the control input u.

References dt, ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::f_, i, ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::SigmaPointCount, t, and u.

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeStatePrediction().

◆ computeSigmaPointMeasurements()

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR >

void ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointMeasurements	(	SigmaPoints< OUTPUT_DIM > &	sigmaMeasurementPoints,
		const ct::core::Time &	t = `0`
	)

Predict measurements of sigma points.

References ct::optcon::EstimatorBase< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::h_, i, ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::SigmaPointCount, and t.

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeMeasurementPrediction().

◆ computePredictionFromSigmaPoints() [1/2]

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

template<size_t DIM>

state_vector_t ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computePredictionFromSigmaPoints ( const SigmaPoints< DIM > & sigmaPoints )

Make a prediction based on sigma points.

◆ computePredictionFromSigmaPoints() [2/2]

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

template<size_t DIM>

auto ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computePredictionFromSigmaPoints ( const SigmaPoints< DIM > & sigmaPoints ) -> state_vector_t

Member Data Documentation

◆ SigmaPointCount

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>

constexpr size_t ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::SigmaPointCount = 2 * STATE_DIM + 1

static

Referenced by ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointMeasurements(), ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeSigmaPointTransition(), and ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >::computeWeights().

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

/home/gim2rng/ct_devel_ws/src/control-toolbox/ct_optcon/include/ct/optcon/filter/UnscentedKalmanFilter.h
/home/gim2rng/ct_devel_ws/src/control-toolbox/ct_optcon/include/ct/optcon/filter/UnscentedKalmanFilter-impl.h

Public Types

Public Member Functions

Static Public Attributes

Additional Inherited Members

Detailed Description

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double> class ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >

Member Typedef Documentation

◆ Base

◆ SigmaPoints

◆ Covariance

◆ CovarianceSquareRoot

◆ control_vector_t

◆ output_matrix_t

◆ output_vector_t

◆ state_matrix_t

◆ state_vector_t

Constructor & Destructor Documentation

◆ UnscentedKalmanFilter() [1/2]

◆ UnscentedKalmanFilter() [2/2]

Member Function Documentation

◆ predict()

◆ update()

◆ computeSigmaPoints()

◆ computeCovarianceFromSigmaPoints()

◆ computeKalmanGain()

◆ updateStateCovariance()

◆ computeStatePrediction()

◆ computeMeasurementPrediction()

◆ computeWeights()

◆ computeSigmaPointTransition()

◆ computeSigmaPointMeasurements()

◆ computePredictionFromSigmaPoints() [1/2]

◆ computePredictionFromSigmaPoints() [2/2]

Member Data Documentation

◆ SigmaPointCount

template<size_t STATE_DIM, size_t CONTROL_DIM, size_t OUTPUT_DIM, typename SCALAR = double>
class ct::optcon::UnscentedKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >