Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Li, Zan; Hossmann, Andreea Maria; Braun, Torsten (2015). Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking (Aachener Informatik-Berichte (AIB) 2015-08). Aachen, Germany: Department of Computer Science of RWTH Aachen University

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Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Item Type:	Report (Report)
Division/Institute:	08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS) 08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor:	Li, Zan, Hossmann, Andreea Maria, Braun, Torsten
Subjects:	000 Computer science, knowledge & systems 500 Science > 510 Mathematics
ISSN:	0935-3232
Series:	Aachener Informatik-Berichte (AIB)
Publisher:	Department of Computer Science of RWTH Aachen University
Language:	English
Submitter:	Dimitrios Xenakis
Date Deposited:	26 May 2015 11:08
Last Modified:	05 Dec 2022 14:47
Additional Information:	Proceedings of the 1st KuVS Expert Talk on Localization: Mathias Pelka, Jo Agila Bitsch, Horst Hellbrück, Klaus Wehrle (editors)
URI:	https://boris.unibe.ch/id/eprint/68816