Indoor localization using unscented Kalman/FIR hybrid filter

Jung Min Pak, Choon Ki Ahn, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper proposes a new nonlinear filtering algorithm that combines the unscented Kalman filter (UKF) and the finite impulse response (FIR) filter. The proposed filter is called the unscented Kalman/FIR hybrid filter (UKFHF). In the UKFHF algorithm, the UKF is used as the main filter, which produces state estimates under ideal conditions. When failures of the UKF are detected, the FIR filter is operated. Using the output of the FIR filter, the UKF is reset and rebooted. In this way, the UKFHF recovers from failures. The proposed UKFHF is applied to indoor human localization using wireless sensor networks. Through simulations, the performance of the UKFHF is demonstrated in comparison with that of the UKF.

Original languageEnglish
Pages (from-to)1057-1063
Number of pages7
JournalJournal of Institute of Control, Robotics and Systems
Volume21
Issue number11
DOIs
Publication statusPublished - 2015 Nov 1

Fingerprint

Impulse Response
Impulse response
Kalman filters
Filter
FIR filters
Kalman Filter
Nonlinear filtering
Wireless sensor networks
Nonlinear Filtering
Wireless Sensor Networks
Output

Keywords

  • Finite impulse response (FIR) filter
  • Indoor localization
  • Unscented Kalman filter
  • Unscented Kalman/FIR hybrid filter

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

Cite this

Indoor localization using unscented Kalman/FIR hybrid filter. / Pak, Jung Min; Ahn, Choon Ki; Lim, Myo Taeg; Song, Moon Kyou.

In: Journal of Institute of Control, Robotics and Systems, Vol. 21, No. 11, 01.11.2015, p. 1057-1063.

Research output: Contribution to journalArticle

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