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  • Research Article
  • Open Access

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

EURASIP Journal on Advances in Signal Processing20062006:043429

  • Received: 1 June 2005
  • Accepted: 23 November 2005
  • Published:


Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-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.


  • High Variability
  • Application Field
  • Quantum Information
  • Position System
  • Regression Technique

Authors’ Affiliations

Department of Electronic Engineering, Technical University of Catalonia, Castelldefels, Barcelona, 08860, Spain
Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, 50018, Spain
Aragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, 50018, Spain
Electronics and Communications Department, University of Zaragoza, Zaragoza, 50018, Spain


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© Casas et al. 2006