EURASIP Journal on Applied Signal Processing
Volume 2006 (2006), Article ID 43429, 8 pages
doi:10.1155/ASP/2006/43429

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

R. Casas,1 A. Marco,2 J. J. Guerrero,2,3 and J. Falcó3,4

1Department of Electronic Engineering, Technical University of Catalonia, Castelldefels, Barcelona 08860, Spain
2Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
3Aragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza 50018 , Spain
4Electronics and Communications Department, University of Zaragoza, Zaragoza 50018, Spain

Received 1 June 2005; Revised 22 November 2005; Accepted 23 November 2005

Copyright © 2006 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.