NDLI: Map-aware RSS localization models and algorithms based on experimental data

Content Provider	IEEE Xplore Digital Library
Author	Montorsi, F. Pancaldi, F. Vitetta, G.M.
Copyright Year	2013
Description	Author affiliation: Dept. of Eng. “Enzo Ferrari”, Univ. of Modena & Reggio Emilia, Modena, Italy (Montorsi, F.; Vitetta, G.M.) \|\| Dept. of Sci. & Methods for Eng., Univ. of Modena & Reggio Emilia, Modena, Italy (Pancaldi, F.)
Abstract	The knowledge of the propagation environment can greatly improve the performance of indoor wireless localization systems, since it can be exploited to cancel out, at least to some extent, the effects of physical obstructions (e.g., walls) degrading the radio signals employed for localization. In indoor localization systems, the propagation environment can be described by maps (e.g., floor plans), which represent a valuable source of knowledge. In this paper a new map-aware statistical model for the measurements acquired in wireless localization systems based on received signal strength is proposed. Experimental results are processed to extract the parameters of the proposed model, which is then exploited to devise map-aware localization algorithms; these are capable of mitigating the non-line-of-sight bias introduced in the measurements by the obstructions modelled by the map. Our numerical results evidence that map-aware modelling can substantially improve localization accuracy in indoor scenarios with respect to map-unaware modelling.
Starting Page	5798
Ending Page	5803
File Size	169075
Page Count	6
File Format	PDF
ISBN	9781467331227
ISSN	15503607
DOI	10.1109/ICC.2013.6655521
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-09
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Noise Wireless communication Atmospheric measurements Particle measurements Numerical models NLOS Localization Map Map-aware RSS
Content Type	Text
Resource Type	Article

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