Algorithmic innovations in extended unbiased FIR filtering of nonlinear models

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

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


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 languageEnglish
Title of host publicationProceedings of the 2015 Science and Information Conference, SAI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781479985470
Publication statusPublished - 2015 Sept 2
EventScience and Information Conference, SAI 2015 - London, United Kingdom
Duration: 2015 Jul 282015 Jul 30


OtherScience and Information Conference, SAI 2015
Country/TerritoryUnited Kingdom


  • 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


Dive into the research topics of 'Algorithmic innovations in extended unbiased FIR filtering of nonlinear models'. Together they form a unique fingerprint.

Cite this