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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages679-683
Number of pages5
ISBN (Print)9780992862633
DOIs
Publication statusPublished - 2015 Dec 22
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 2015 Aug 312015 Sep 4

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period15/8/3115/9/4

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ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EUSIPCO.2015.7362469