ekf Namespace Reference

Classes
class	EKF_Filter
	Extended Kalman Filter (EKF) class for state estimation. More...

Functions
Matrix< double, DIM_X, 1 >	predictionModel (const Matrix< double, DIM_X, 1 > &currentState)
	Prediction model for the Extended Kalman Filter (EKF). More...
Matrix< double, DIM_Z, DIM_X >	calculateJacobianMatrix (const Matrix< double, DIM_X, 1 > &vecX)
	Calculate the Jacobian matrix for the measurement model. More...
Matrix< double, DIM_Z, 1 >	calculateMeasurement (const Matrix< double, DIM_X, 1 > &currentPosition)
	Calculate the measurement vector. More...

Variables
static uint32_t	lastTime = millis()
static Matrix< double, DIM_X, 1 >	x_kminus1 = (Eigen::Matrix<double, DIM_X, 1>() << 0.0, 0.0, 0.0).finished()
static Matrix< double, NUM_LANDMARKS, 3 >	landmarkPositions { Matrix<double, NUM_LANDMARKS, 3>::Zero() }

Function Documentation

calculateJacobianMatrix()

Matrix< double, DIM_Z, DIM_X > calculateJacobianMatrix

(

const Matrix< double, DIM_X, 1 > &

vecX

)

Calculate the Jacobian matrix for the measurement model.

Parameters

vecX	The state vector.

Returns

The Jacobian matrix.

calculateMeasurement()

Matrix< double, DIM_Z, 1 > calculateMeasurement

(

const Matrix< double, DIM_X, 1 > &

currentPosition

)

Calculate the measurement vector.

Parameters

currentPosition

The current position.

Returns

The measurement vector.

predictionModel()

Matrix< double, DIM_X, 1 > predictionModel

(

const Matrix< double, DIM_X, 1 > &

currentState

)

Prediction model for the Extended Kalman Filter (EKF).

Parameters

currentState

The current state vector.

Returns

The predicted state vector.

Variable Documentation

landmarkPositions

Matrix<double, NUM_LANDMARKS, 3> landmarkPositions { Matrix<double, NUM_LANDMARKS, 3>::Zero() }

static

lastTime

uint32_t lastTime = millis()

static

x_kminus1

Matrix<double, DIM_X, 1> x_kminus1 = (Eigen::Matrix<double, DIM_X, 1>() << 0.0, 0.0, 0.0).finished()

static