kfPredict: Kalman Filter Prediction step

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

Description

This function performs the Kalman Filter prediction step

Usage

kfPredict(x, P, A, Q, B, u)

Arguments

x	An N x 1 mean state estimate of previous step
P	An N x N state covariance of previous step
A	(Optional, default idendity) transition matrix of the discrete model
Q	(Optional, default zero) process noise of discrete model
B	(Optional, default idendity) input effect matrix
u	(Optional, default empty) constant input

Value

A list with two elements

X

the predicted state mean, and

P

the predicted state covariance.

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

kfUpdate, ltiDisc 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.