Class to perform EKF time update step. More...

Collaboration diagram for clockwerk::EkfTimeUpdate< T, N >:

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Public Member Functions
int	runUpdate (T time_prev, const std::array< T, N > &state_prev, const Matrix< T, N, N > &cov_prev, T time_up, std::array< T, N > state_up, Matrix< T, N, N > cov_up)
	Function to run a single extended kalman filter time update step.

Public Attributes
Integrator< T, N+N N >	integrator_ptr = nullptr
	The integrator to be used internally by the EKF stepper. This integrator should be pre-configured to use a rates function with size N + N*N to propagate dynamics In most cases should probably be RK4.

Matrix< T, N, N >	phi
	Optional return of the phi matrix calculated by EKF.

Detailed Description

template<typename T, unsigned int N>
class clockwerk::EkfTimeUpdate< T, N >

Class to perform EKF time update step.

Member Function Documentation

template<typename T , unsigned int N>

int clockwerk::EkfTimeUpdate< T, N >::runUpdate	(	T	time_prev,
		const std::array< T, N > &	state_prev,
		const Matrix< T, N, N > &	cov_prev,
		T	time_up,
		std::array< T, N > *	state_up,
		Matrix< T, N, N > *	cov_up
	)

Function to run a single extended kalman filter time update step.

Parameters

time_prev	The time at which the previous state and covariance are known
state_prev	The previous state as an N element state vector
cov_prev	The previous covariance as an N*N matrix
time_up	The time to which the state and covariance will be propagated
state_up	The updated state at time time_up
cov_up	The updated covariance at time time_up

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