FKF.SP: Fast Kalman Filtering Through Sequential Processing

Thomas Aspinall; Adrian Gepp; Geoffrey Harris; Simone Kelly; Colette Southam; Bruce J Vanstone; David Luethi; Philipp Erb; Simon Otziger; Paul Smith

FKF.SP: Fast Kalman Filtering Through Sequential Processing

Thomas Aspinall (Developer)
, Adrian Gepp
, Geoffrey Harris
, Simone Kelly
, Colette Southam
, Bruce J Vanstone
, David Luethi (Other)
, Philipp Erb (Other)
, Simon Otziger (Other)
, Paul Smith (Other)

Research output: Non-textual form › Software › Research

Abstract

Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. 'FKF.SP' was built upon the existing 'FKF' package and was designed to generally increase the computational efficiency of Kalman filtering when independence is assumed in the measurement error of observations. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8)

Original language	English
Media of output	Online
Publication status	Published - 18 Dec 2020

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https://github.com/TomAspinall/FKF.SPLicence: Free to read

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title = "FKF.SP: Fast Kalman Filtering Through Sequential Processing",

abstract = "Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. 'FKF.SP' was built upon the existing 'FKF' package and was designed to generally increase the computational efficiency of Kalman filtering when independence is assumed in the measurement error of observations. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8)",

author = "Thomas Aspinall and Adrian Gepp and Geoffrey Harris and Simone Kelly and Colette Southam and Vanstone, \{Bruce J\} and David Luethi and Philipp Erb and Simon Otziger and Paul Smith",

year = "2020",

month = dec,

day = "18",

language = "English",

}

TY - ADVS

T1 - FKF.SP: Fast Kalman Filtering Through Sequential Processing

AU - Gepp, Adrian

AU - Harris, Geoffrey

AU - Kelly, Simone

AU - Southam, Colette

AU - Vanstone, Bruce J

A2 - Aspinall, Thomas

A2 - Luethi, David

A2 - Erb, Philipp

A2 - Otziger, Simon

A2 - Smith, Paul

PY - 2020/12/18

Y1 - 2020/12/18

N2 - Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. 'FKF.SP' was built upon the existing 'FKF' package and was designed to generally increase the computational efficiency of Kalman filtering when independence is assumed in the measurement error of observations. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8)

AB - Fast and flexible Kalman filtering implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. 'FKF.SP' was built upon the existing 'FKF' package and was designed to generally increase the computational efficiency of Kalman filtering when independence is assumed in the measurement error of observations. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8)

UR - https://cran.r-project.org/package=FKF.SP

UR - https://cran.r-project.org/web/packages/FKF.SP/FKF.SP.pdf

UR - https://cran.r-project.org/web/packages/FKF.SP/vignettes/FKFSP.html

M3 - Software

ER -

FKF.SP: Fast Kalman Filtering Through Sequential Processing

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