Algorithmic innovations in extended unbiased FIR filtering of nonlinear models

Moises Granados-Cruz; Yuriy S. Shmaliy; Choon Ki Ahn; Shunyi Zhao

doi:10.1109/SAI.2015.7237332

Algorithmic innovations in extended unbiased FIR filtering of nonlinear models

Moises Granados-Cruz, Yuriy S. Shmaliy, Choon Ki Ahn, Shunyi Zhao

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Two algorithms of extended unbiased FIR (EFIR) filtering are proposed for nonlinear state estimation. The first algorithm is basic and the second one employs the nonlinear-to-linear observation conversion obtained by the batch EFIR filter with minimum memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness, but require the optimal horizon. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.

Original language	English
Title of host publication	Proceedings of the 2015 Science and Information Conference, SAI 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1420-1423
Number of pages	4
ISBN (Print)	9781479985470
DOIs	https://doi.org/10.1109/SAI.2015.7237332
Publication status	Published - 2015 Sept 2
Event	Science and Information Conference, SAI 2015 - London, United Kingdom Duration: 2015 Jul 28 → 2015 Jul 30

Other

Other	Science and Information Conference, SAI 2015
Country/Territory	United Kingdom
City	London
Period	15/7/28 → 15/7/30

Keywords

Extended FIR filtering
Extended Kalman filtering
Nonlinear estimation
Nonlinear-to-linear conversion

ASJC Scopus subject areas

Health Informatics
Social Sciences (miscellaneous)
Computer Science Applications
Human-Computer Interaction
Computer Networks and Communications
Information Systems
Software

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SAI.2015.7237332

Cite this

Algorithmic innovations in extended unbiased FIR filtering of nonlinear models. / Granados-Cruz, Moises; Shmaliy, Yuriy S.; Ahn, Choon Ki et al.
Proceedings of the 2015 Science and Information Conference, SAI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1420-1423 7237332.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Granados-Cruz, M, Shmaliy, YS, Ahn, CK & Zhao, S 2015, Algorithmic innovations in extended unbiased FIR filtering of nonlinear models. in Proceedings of the 2015 Science and Information Conference, SAI 2015., 7237332, Institute of Electrical and Electronics Engineers Inc., pp. 1420-1423, Science and Information Conference, SAI 2015, London, United Kingdom, 15/7/28. https://doi.org/10.1109/SAI.2015.7237332

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title = "Algorithmic innovations in extended unbiased FIR filtering of nonlinear models",

abstract = "Two algorithms of extended unbiased FIR (EFIR) filtering are proposed for nonlinear state estimation. The first algorithm is basic and the second one employs the nonlinear-to-linear observation conversion obtained by the batch EFIR filter with minimum memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness, but require the optimal horizon. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.",

keywords = "Extended FIR filtering, Extended Kalman filtering, Nonlinear estimation, Nonlinear-to-linear conversion",

author = "Moises Granados-Cruz and Shmaliy, {Yuriy S.} and Ahn, {Choon Ki} and Shunyi Zhao",

year = "2015",

month = sep,

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doi = "10.1109/SAI.2015.7237332",

language = "English",

isbn = "9781479985470",

pages = "1420--1423",

booktitle = "Proceedings of the 2015 Science and Information Conference, SAI 2015",

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AU - Zhao, Shunyi

PY - 2015/9/2

Y1 - 2015/9/2

N2 - Two algorithms of extended unbiased FIR (EFIR) filtering are proposed for nonlinear state estimation. The first algorithm is basic and the second one employs the nonlinear-to-linear observation conversion obtained by the batch EFIR filter with minimum memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness, but require the optimal horizon. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.

AB - Two algorithms of extended unbiased FIR (EFIR) filtering are proposed for nonlinear state estimation. The first algorithm is basic and the second one employs the nonlinear-to-linear observation conversion obtained by the batch EFIR filter with minimum memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness, but require the optimal horizon. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.

KW - Extended FIR filtering

KW - Extended Kalman filtering

KW - Nonlinear estimation

KW - Nonlinear-to-linear conversion

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DO - 10.1109/SAI.2015.7237332

M3 - Conference contribution

AN - SCOPUS:84957824897

SN - 9781479985470

SP - 1420

EP - 1423

BT - Proceedings of the 2015 Science and Information Conference, SAI 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Science and Information Conference, SAI 2015

Y2 - 28 July 2015 through 30 July 2015

ER -

Algorithmic innovations in extended unbiased FIR filtering of nonlinear models

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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