New results in nonlinear state estimation using extended unbiased fir filtering

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

doi:10.1109/EUSIPCO.2015.7362469

New results in nonlinear state estimation using extended unbiased fir filtering

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

School of Electrical Engineering

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

2 Citations (Scopus)

Abstract

This paper discusses two algorithms of extended unbiased FIR (EFIR) filtering of nonlinear discrete-time state-space models used in tracking and state estimation. The basic algorithm employs the extended nonlinear state and observation equations. The modified algorithm utilizes the nonlinear-to-linear conversion of the observation equation which is provided using a batch EFIR filter having small memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness against temporary model uncertainties. These algorithms require an optimal horizon in order to minimize the mean square error. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.

Original language	English
Title of host publication	2015 23rd European Signal Processing Conference, EUSIPCO 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	679-683
Number of pages	5
ISBN (Electronic)	9780992862633
DOIs	https://doi.org/10.1109/EUSIPCO.2015.7362469
Publication status	Published - 2015 Dec 22
Event	23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: 2015 Aug 31 → 2015 Sept 4

Publication series

Name	2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other	23rd European Signal Processing Conference, EUSIPCO 2015
Country/Territory	France
City	Nice
Period	15/8/31 → 15/9/4

ASJC Scopus subject areas

Media Technology
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/EUSIPCO.2015.7362469

Cite this

Granados-Cruz, M., Shmaliy, Y. S., Khan, S. H., Ahn, C. K., & Zhao, S. (2015). New results in nonlinear state estimation using extended unbiased fir filtering. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015 (pp. 679-683). [7362469] (2015 23rd European Signal Processing Conference, EUSIPCO 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EUSIPCO.2015.7362469

New results in nonlinear state estimation using extended unbiased fir filtering. / Granados-Cruz, Moises; Shmaliy, Yuriy S.; Khan, Sanowar H. et al.
2015 23rd European Signal Processing Conference, EUSIPCO 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 679-683 7362469 (2015 23rd European Signal Processing Conference, EUSIPCO 2015).

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

Granados-Cruz, M, Shmaliy, YS, Khan, SH, Ahn, CK & Zhao, S 2015, New results in nonlinear state estimation using extended unbiased fir filtering. in 2015 23rd European Signal Processing Conference, EUSIPCO 2015., 7362469, 2015 23rd European Signal Processing Conference, EUSIPCO 2015, Institute of Electrical and Electronics Engineers Inc., pp. 679-683, 23rd European Signal Processing Conference, EUSIPCO 2015, Nice, France, 15/8/31. https://doi.org/10.1109/EUSIPCO.2015.7362469

Granados-Cruz M, Shmaliy YS, Khan SH, Ahn CK, Zhao S. New results in nonlinear state estimation using extended unbiased fir filtering. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 679-683. 7362469. (2015 23rd European Signal Processing Conference, EUSIPCO 2015). doi: 10.1109/EUSIPCO.2015.7362469

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abstract = "This paper discusses two algorithms of extended unbiased FIR (EFIR) filtering of nonlinear discrete-time state-space models used in tracking and state estimation. The basic algorithm employs the extended nonlinear state and observation equations. The modified algorithm utilizes the nonlinear-to-linear conversion of the observation equation which is provided using a batch EFIR filter having small memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness against temporary model uncertainties. These algorithms require an optimal horizon in order to minimize the mean square error. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.",

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New results in nonlinear state estimation using extended unbiased fir filtering

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