Implements a discrete-time EKF. More...

#include <ExtendedKalmanFilter.h>

Inheritance diagram for filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >:

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Collaboration diagram for filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >:

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Public Types
using	state_t = Eigen::Matrix<double, stateDim, 1>

using	output_t = Eigen::Matrix<double, outputDim, 1>

using	input_t = Eigen::Matrix<double, inputDim, 1>

using	statefunc_t

using	outputfunc_t

using	processnoise_t = Eigen::Matrix<double, processNoiseDim, 1>

using	outputnoise_t = Eigen::Matrix<double, outputNoiseDim, 1>

Public Member Functions
	ExtendedKalmanFilter (const statefunc_t &stateFunc, const outputfunc_t &outputFunc, const statespace::NoiseCovMat< stateDim, processNoiseDim, inputDim > &processNoise, const statespace::NoiseCovMat< stateDim, outputNoiseDim, outputDim > &outputNoise, double dt)
	Create a new discrete-time EKF.

void	predict (const Eigen::Matrix< double, inputDim, 1 > &input) override
	Use the model to predict the next system state, given the current inputs.

void	correct (const Eigen::Matrix< double, outputDim, 1 > &measurement)
	Correct the state estimate with measurement data.

Public Member Functions inherited from filters::KalmanFilterBase< stateDim, inputDim >
void	reset ()
	Reset the filter.

void	reset (const Eigen::Matrix< double, stateDim, 1 > &state)
	Reset the filter.

void	reset (const Eigen::Matrix< double, stateDim, 1 > &state, const Eigen::Matrix< double, stateDim, 1 > &stdDevs)
	Reset the filter.

void	reset (const Eigen::Matrix< double, stateDim, 1 > &state, const Eigen::Matrix< double, stateDim, stateDim > &estCovMat)
	Reset the filter.

Eigen::Matrix< double, stateDim, 1 >	getState () const
	Gets the current state estimate.

Eigen::Matrix< double, stateDim, stateDim >	getEstimateCovarianceMat () const
	Get the current estimate covariance matrix.

Public Attributes
std::function< Eigen::Matrix< double, stateDim, stateDim >(const state_t &, const input_t &, const processnoise_t &)>	stateFuncJacobianX
	Set this to provide an analytic solution to df/dx.

std::function< Eigen::Matrix< double, stateDim, processNoiseDim >(const state_t &, const input_t &, const processnoise_t &)>	stateFuncJacobianW
	Set this to provide an analytic solution to df/dw.

std::function< Eigen::Matrix< double, outputDim, stateDim >(const state_t &, const outputnoise_t &)>	outputFuncJacobianX
	Set this to provide an analytic solution to dh/dx.

std::function< Eigen::Matrix< double, outputDim, outputNoiseDim >(const state_t &, const outputnoise_t &)>	outputFuncJacobianV
	Set this to provide an analytic solution to dh/dv.

Additional Inherited Members
Protected Attributes inherited from filters::KalmanFilterBase< stateDim, inputDim >
Eigen::Matrix< double, stateDim, stateDim >	P

Eigen::Matrix< double, stateDim, 1 >	xHat

Detailed Description

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>
class filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >

Implements a discrete-time EKF.

This implements the more general system form described here: https://en.wikipedia.org/wiki/Extended_Kalman_filter#Non-additive_noise_formulation_and_equations

Template Parameters

stateDim	The dimension of the state space for this system. This is the number of elements in the state vector.
inputDim	The dimension of the input space for this system. This is the number of elements in the input vector.
outputDim	The dimension of the output space for this system. This is the number of elements in the output (measurement) vector.
processNoiseDim	The dimension of the process noise matrix. In systems with additive process noise, this is the same as stateDim.
outputNoiseDim	The dimension of the output noise matrix. In systems with additive output noise, this is the same as outputDim.

Member Typedef Documentation

◆ outputfunc_t

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

using filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::outputfunc_t

Initial value:

std::function<output_t(

const state_t&, const Eigen::Matrix<double, outputNoiseDim, 1>&)>

Eigen::Matrix

◆ statefunc_t

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

using filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::statefunc_t

Initial value:

std::function<state_t(

const state_t&, const input_t&, const Eigen::Matrix<double, processNoiseDim, 1>&)>

Constructor & Destructor Documentation

◆ ExtendedKalmanFilter()

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::ExtendedKalmanFilter	(	const statefunc_t &	stateFunc,
		const outputfunc_t &	outputFunc,
		const statespace::NoiseCovMat< stateDim, processNoiseDim, inputDim > &	processNoise,
		const statespace::NoiseCovMat< stateDim, outputNoiseDim, outputDim > &	outputNoise,
		double	dt )

inline

Create a new discrete-time EKF.

Parameters

stateFunc	Discrete-time state transition function. x_t+1 = f(x_t, u)
outputFunc	Output function of the system. Also known as h(x)
processNoise	The process noise covariance matrix for this system.
outputNoise	The output noise covariance matrix for this system.
dt	The time in seconds between updates.

Member Function Documentation

◆ correct()

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

void filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::correct ( const Eigen::Matrix< double, outputDim, 1 > & measurement )

inline

Correct the state estimate with measurement data.

The measurement should be in the same space as the state.

Parameters

measurement The measurement to use to correct the filter.

◆ predict()

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

void filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::predict ( const Eigen::Matrix< double, inputDim, 1 > & input )

inlineoverridevirtual

Use the model to predict the next system state, given the current inputs.

Parameters

input The current inputs to the system.

Implements filters::KalmanFilterBase< stateDim, inputDim >.

Member Data Documentation

◆ outputFuncJacobianV

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

std::function<Eigen::Matrix<double, outputDim, outputNoiseDim>(const state_t&, const outputnoise_t&)> filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::outputFuncJacobianV

Set this to provide an analytic solution to dh/dv.

If this is null, it will be numerically approximated.

◆ outputFuncJacobianX

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

std::function<Eigen::Matrix<double, outputDim, stateDim>(const state_t&, const outputnoise_t&)> filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::outputFuncJacobianX

Set this to provide an analytic solution to dh/dx.

If this is null, it will be numerically approximated.

◆ stateFuncJacobianW

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

std::function<Eigen::Matrix<double, stateDim, processNoiseDim>( const state_t&, const input_t&, const processnoise_t&)> filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::stateFuncJacobianW

Set this to provide an analytic solution to df/dw.

If this is null, it will be numerically approximated.

◆ stateFuncJacobianX

template<int stateDim, int inputDim, int outputDim, int processNoiseDim, int outputNoiseDim>

std::function<Eigen::Matrix<double, stateDim, stateDim>(const state_t&, const input_t&, const processnoise_t&)> filters::ExtendedKalmanFilter< stateDim, inputDim, outputDim, processNoiseDim, outputNoiseDim >::stateFuncJacobianX

Set this to provide an analytic solution to df/dx.

If this is null, it will be numerically approximated.

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

src/filters/ExtendedKalmanFilter.h

Public Types

Public Member Functions

Public Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ outputfunc_t

◆ statefunc_t

Constructor & Destructor Documentation

◆ ExtendedKalmanFilter()

Member Function Documentation

◆ correct()

◆ predict()

Member Data Documentation

◆ outputFuncJacobianV

◆ outputFuncJacobianX

◆ stateFuncJacobianW

◆ stateFuncJacobianX