@inproceedings{95733bccc78546d2a9ffbc8d3eea39c0,
title = "Estimation of Spatial Fields of Nlos/Los Conditions for Improved Localization in Indoor Environments",
abstract = "A major challenge in indoor localization is the presence or absence of line-of-sight (LOS). The absence of LOS, denoted as non-line-of-sight (NLOS), directly affects the accuracy of any localization algorithm because of the induced bias in ranging. The estimation of the spatial distribution of NLOS-induced ranging bias in indoor environments remains a major challenge. In this paper, we propose a novel crowd-based Bayesian learning approach to the estimation of bias fields caused by LOS/NLOS conditions. The proposed method is based on the concept of Gaussian processes and exploits numerous measurements. The performance of the method is demonstrated with extensive experiments.",
keywords = "crowd sourcing, Gaussian processes, Indoor localization, NLOS, spatial field",
author = "Eva Arias-De-Reyna and Davide Dardari and Pau Closas and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 20th IEEE Statistical Signal Processing Workshop, SSP 2018 ; Conference date: 10-06-2018 Through 13-06-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/SSP.2018.8450840",
language = "English",
isbn = "9781538615706",
series = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "233--237",
booktitle = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",
}