Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering

Jung Min Pak, Choon Ki Ahn, Yuriy S. Shmaliy, Peng Shi, Myo Taeg Lim

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.

Original languageEnglish
JournalIEEE Transactions on Human-Machine Systems
DOIs
Publication statusAccepted/In press - 2016 Oct 11

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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