This article is part of the series Signal Processing for Location Estimation and Tracking in Wireless Environments.

Open Access Research Article

NLOS Identification and Weighted Least-Squares Localization for UWB Systems Using Multipath Channel Statistics

İsmail Güvenç*, Chia-Chin Chong, Fujio Watanabe and Hiroshi Inamura

Author Affiliations

DoCoMo Communications Laboratories USA, Inc., 3240 Hillview Avenue, Palo Alto, CA 94304, USA

For all author emails, please log on.

EURASIP Journal on Advances in Signal Processing 2008, 2008:271984  doi:10.1155/2008/271984


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/271984


Received: 30 March 2007
Revisions received: 6 July 2007
Accepted: 21 July 2007
Published: 2 August 2007

© 2008 The Author(s).

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

Non-line-of-sight (NLOS) identification and mitigation carry significant importance in wireless localization systems. In this paper, we propose a novel NLOS identification technique based on the multipath channel statistics such as the kurtosis, the mean excess delay spread, and the root-mean-square delay spread. In particular, the IEEE 802.15.4a ultrawideband channel models are used as examples and the above statistics are found to be well modeled by log-normal random variables. Subsequently, a joint likelihood ratio test is developed for line-of-sight (LOS) or NLOS identification. Three different weighted least-squares (WLSs) localization techniques that exploit the statistics of multipath components (MPCs) are analyzed. The basic idea behind the proposed WLS approaches is that smaller weights are given to the measurements which are likely to be biased (based on the MPC information), as opposed to variance-based WLS techniques in the literature. Accuracy gains with respect to the conventional least-squares algorithm are demonstrated via Monte-Carlo simulations and verified by theoretical derivations.

Publisher note

To access the full article, please see PDF.