Robust estimator for non-line-of-sight error mitigation in indoor localization

  • Authors:

  • R. Casas;A. Marco;J. J. Guerrero;J. Falcó

  • Affiliations:

  • Department of Electronic Engineering, Technical University of Catalonia, Castelldefels, Barcelona, Spain;Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain;Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain and Aragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain;Aragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain and Electronics and Communications Department, University of Zaragoza, Zaragoza, Spain

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2006

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Abstract

Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from nonline-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.