kfUpdate: Kalman Filter measurement update step

In eddelbuettel/rcppkalman: 'RcppArmadillo'-Based Kalman Filtering

Description

This function performs the Kalman Filter measurement update step

Usage

kfUpdate(x, P, y, H, R)

Arguments

x	An N x 1 mean state estimate after prediction step
P	An N x N state covariance after prediction step
y	A D x 1 measurement vector.
H	Measurement matrix.
R	Measurement noise covariance.

Details

This functions performs the Kalman Filter measurement update step.

Value

A list with elements

X

the update state mean,

P

the update state covariance,

K

the computed Kalman gain,

IM

the mean of the predictive distribution of Y, and

IS

the covariance of the predictive distribution of Y

Author(s)

The EKF/UKF Toolbox was written by Simo Särkkä, Jouni Hartikainen, and Arno Solin.

Dirk Eddelbuettel is porting this package to R and C++, and maintaing it.

See Also

kfPredict and the documentation for the EKF/UKF toolbox at http://becs.aalto.fi/en/research/bayes/ekfukf

eddelbuettel/rcppkalman documentation built on June 19, 2020, 4:28 a.m.