ESTIMATION OF SPATIAL FIELDS OF NLOS/LOS CONDITIONS FOR IMPROVED LOCALIZATION IN INDOOR ENVIRONMENTS

被引：0

作者：

Arias-de-Reyna, Eva ^{[1
]}

Dardari, Davide ^{[2
]}

Closas, Pau ^{[3
]}

Djuric, Petar M. ^{[4
]}

机构：

[1] Univ Seville, Dept Signal Theory & Commun, Seville, Spain

[2] Univ Bologna, Dept Elect Elect & Informat Eng DEI & CNIT, Cesena, Italy

[3] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA USA

[4] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY USA

来源：

2018 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2018年

基金：

欧盟地平线“2020”;

关键词：

Indoor localization; crowd sourcing; Gaussian processes; spatial field; NLOS; ALGORITHM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：658 / 662

页数：5

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