Affiliations 

  • 1 School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia. anbalagan@student.usm.my
  • 2 School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia. syazreen@usm.my
  • 3 School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia. eepatrick@usm.my
Sensors (Basel), 2019 Nov 01;19(21).
PMID: 31683837 DOI: 10.3390/s19214748

Abstract

This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node's translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.