TY - JOUR
T1 - Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms
AU - Martínez-Gómez, Jesus
AU - Del Horno, Miguel Martínez
AU - Castillo-Cara, Manuel
AU - Luján, Víctor Manuel Brea
AU - Barbosa, Luis Orozco
AU - García-Varea, Ismael
N1 - Publisher Copyright:
© 2016 The Author(s).
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.
AB - The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.
UR - http://www.scopus.com/inward/record.url?scp=84984889419&partnerID=8YFLogxK
U2 - 10.1177/1550147716661953
DO - 10.1177/1550147716661953
M3 - Artículo (Contribución a Revista)
AN - SCOPUS:84984889419
SN - 1550-1329
VL - 12
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
IS - 8
ER -