NAV::KalmanFilter Class Reference

Generalized Kalman Filter class. More...

Public Member Functions
void	correct ()
	Do a Measurement Update with a Measurement 𝐳
void	correctWithMeasurementInnovation ()
	Do a Measurement Update with a Measurement Innovation 𝜹𝐳
	KalmanFilter ()=delete
	Default constructor.
	KalmanFilter (int n, int m)
	Constructor.
void	predict ()
	Do a Time Update.
void	setZero ()
	Sets all Vectors and matrices to 0.

Static Public Member Functions
static Eigen::MatrixXd	transitionMatrix (const Eigen::MatrixXd &F, double tau_s)
	Updates the state transition matrix 𝚽 limited to first order in 𝐅𝜏ₛ

Public Attributes
Eigen::MatrixXd	H
	𝐇 Measurement sensitivity Matrix
Eigen::MatrixXd	K
	𝐊 Kalman gain matrix
Eigen::MatrixXd	P
	𝐏 Error covariance matrix
Eigen::MatrixXd	Phi
	𝚽 State transition matrix
Eigen::MatrixXd	Q
	𝐐 System/Process noise covariance matrix
Eigen::MatrixXd	R
	𝐑 = 𝐸{𝐰ₘ𝐰ₘᵀ} Measurement noise covariance matrix
Eigen::MatrixXd	S
	𝗦 Measurement prediction covariance matrix
Eigen::MatrixXd	x
	x̂ State vector
Eigen::MatrixXd	z
	𝐳 Measurement vector

Detailed Description

Generalized Kalman Filter class.

Constructor & Destructor Documentation

KalmanFilter()

NAV::KalmanFilter::KalmanFilter ( int n, int m )

inline

Constructor.

Parameters

[in]	n	Number of States
[in]	m	Number of Measurements

Member Function Documentation

correct()

void NAV::KalmanFilter::correct ( )

inline

Do a Measurement Update with a Measurement 𝐳

Attention

Update the Measurement sensitivity Matrix (𝐇), the Measurement noise covariance matrix (𝐑) and the Measurement vector (𝐳) before calling this

Note

See P. Groves (2013) - Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (ch. 3.2.2)

correctWithMeasurementInnovation()

void NAV::KalmanFilter::correctWithMeasurementInnovation ( )

inline

Do a Measurement Update with a Measurement Innovation 𝜹𝐳

Attention

Update the Measurement sensitivity Matrix (𝐇), the Measurement noise covariance matrix (𝐑) and the Measurement vector (𝐳) before calling this

Note

See P. Groves (2013) - Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (ch. 3.2.2)

See Brown & Hwang (2012) - Introduction to Random Signals and Applied Kalman Filtering (ch. 5.5 - figure 5.5)

predict()

void NAV::KalmanFilter::predict ( )

inline

Do a Time Update.

Attention

Update the State transition matrix (𝚽) and the Process noise covariance matrix (𝐐) before calling this

Note

See P. Groves (2013) - Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (ch. 3.2.2)

transitionMatrix()

static Eigen::MatrixXd NAV::KalmanFilter::transitionMatrix ( const Eigen::MatrixXd & F, double tau_s )

inlinestatic

Updates the state transition matrix 𝚽 limited to first order in 𝐅𝜏ₛ

Parameters

[in]	F	System Matrix
[in]	tau_s	time interval in [s]

Note

See Groves (2013) chapter 14.2.4, equation (14.72)

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

• /home/runner/work/INSTINCT/INSTINCT/src/Navigation/Math/KalmanFilter.hpp