Indoor Positioning Systems Based on a Modified Matrix-Weighted Fusion Estimator With Multipath and NLOS Mitigation

Li Li*, Dan Zhao, Yuanqing Xia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, an indoor positioning system is investigated for an unmanned ground vehicle based on inertial measurement unit (IMU) and ultrawide band (UWB) integration. Measurement outliers of UWB usually exist due to multipath and non-line-of-sight (NLOS) effects. To describe characteristics of stochastic noises well, heavy-tailed noises modeled by Student's t distributions are considered. By a NLOS identification method, a local modified fusion estimator is designed based on the Kalman filter framework to fuse the measurements of IMU and UWB. A modified matrix-weighted fusion estimator is proposed for high-accuracy estimation and robustness. Sufficient conditions are given for the fusion estimation error to be exponentially bounded in mean square. Finally, effectiveness of the proposed fusion estimator is indicated by experimental results.

Original languageEnglish
Pages (from-to)40041-40050
Number of pages10
JournalIEEE Internet of Things Journal
Volume11
Issue number24
DOIs
Publication statusPublished - 2024

Keywords

  • Fusion estimator
  • heavy-tailed noises
  • indoor positioning system
  • non line of sight (NLOS)

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