rtsSmoother: Rauch-Tung-Striebel smoother

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

Description

This function computes the Rauch-Tung-Striebel smoother.

Usage

rtsSmoother(M, P, A, Q)

Arguments

M	An N x K matrix of K mean estimates from the Kalman Filter
P	An N x N x K cube length K with N x N state covariances matrices from the Kalman Filter
A	An N x N state transition matrix (or in the more general case a list of K such matrices; not yet implemented)
Q	An N x N noise covariance matrix (or in the more general case a list of K such matrices; not yet implemented)

Details

This function implements the Rauch-Tung-Striebel smoother algorithm which calculate a “smoothed” sequence from the given Kalman filter output sequence by conditioning all steps to all measurements.

Value

A list with three elements

SM

the smoothed mean sequence,

SP

the smooted state covariance sequence,and

D

the smoothed gain sequence.

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, kfUpdate, 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.