Steady State Kalman Filter is a time invariant linear estimator. It starts with the same update rule as the standard Kalman Filter, but instead of propagating the covariance and estimate through time, it assumes convergence reducing the problem to solving an Algebraic Ricatti Equation. More...

#include <SteadyStateKalmanFilter.h>

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

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

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
	SteadyStateKalmanFilter (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f, std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h, const state_matrix_t &Q, const output_matrix_t &R, const state_vector_t &x0=state_vector_t::Zero(), size_t maxDAREIterations=1000)
	Constructor. More...

	SteadyStateKalmanFilter (std::shared_ptr< SystemModelBase< STATE_DIM, CONTROL_DIM, SCALAR >> f, std::shared_ptr< LinearMeasurementModel< OUTPUT_DIM, STATE_DIM, SCALAR >> h, const SteadyStateKalmanFilterSettings< STATE_DIM, SCALAR > &sskf_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...

void	setMaxDAREIterations (size_t maxDAREIterations)
	Limit number of iterations of the DARE solver. More...

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...

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::SteadyStateKalmanFilter< STATE_DIM, CONTROL_DIM, OUTPUT_DIM, SCALAR >

Steady State Kalman Filter is a time invariant linear estimator. It starts with the same update rule as the standard Kalman Filter, but instead of propagating the covariance and estimate through time, it assumes convergence reducing the problem to solving an Algebraic Ricatti Equation.