Statistical indoor location estimation for the NLoS environment using radial extreme value weibull distribution

Kosuke Okusa, Toshinari Kamakura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this study, we investigate the possibility of analyzing indoor location estimation under the NLoS environment by radial extreme value distribution model. In this study, we assume that the observed distance between the transmitter and receiver is a statistical radial extreme value distribution. The proposed method is based on the marginal likelihoods of radial extreme value distribution generated by positive distribution among several transmitter radio sites placed in a room. To demonstrate the effectiveness of the proposed method, we carried out a simulation study and conducted two sets of experiments to assess the accuracy of the location estimation for the static case and dynamic case. In the static experiment, the subject was stationary in some places within the chamber. In this experiment, we were able to demonstrate the precise performance of the proposed method. In the dynamic experiment, the subject was moved around within the chamber. In this experiment, we were able to determine the suitability of the proposed method for practical use. Results indicate that high accuracy was achieved when the method was implemented for indoor spatial location estimation.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering 2017, WCE 2017
EditorsA. M. Korsunsky, Andrew Hunter, Len Gelman, S. I. Ao, David WL Hukins
PublisherNewswood Limited
Pages555-560
Number of pages6
ISBN (Electronic)9789881404831
Publication statusPublished - Jan 1 2017
Event2017 World Congress on Engineering, WCE 2017 - London, United Kingdom
Duration: Jul 5 2017Jul 7 2017

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2230
ISSN (Print)2078-0958

Other

Other2017 World Congress on Engineering, WCE 2017
CountryUnited Kingdom
CityLondon
Period7/5/177/7/17

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All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

Cite this

Okusa, K., & Kamakura, T. (2017). Statistical indoor location estimation for the NLoS environment using radial extreme value weibull distribution. In A. M. Korsunsky, A. Hunter, L. Gelman, S. I. Ao, & D. WL. Hukins (Eds.), Proceedings of the World Congress on Engineering 2017, WCE 2017 (pp. 555-560). (Lecture Notes in Engineering and Computer Science; Vol. 2230). Newswood Limited.