TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Robust statistical approaches for RSS-based floor detection in indoor localization

Tutkimustuotosvertaisarvioitu

Standard

Robust statistical approaches for RSS-based floor detection in indoor localization. / Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona.

julkaisussa: Sensors, Vuosikerta 16, Nro 6, 793, 01.06.2016.

Tutkimustuotosvertaisarvioitu

Harvard

APA

Vancouver

Author

Bibtex - Lataa

@article{0290ac01e8a542acac32998d943a913b,
title = "Robust statistical approaches for RSS-based floor detection in indoor localization",
abstract = "Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heardWiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.",
keywords = "Floor detection, Indoor localization, Robust regression, RSS-based localization, Trilateration, Weighted centroid localization",
author = "Alireza Razavi and Mikko Valkama and Lohan, {Elena Simona}",
year = "2016",
month = "6",
day = "1",
doi = "10.3390/s16060793",
language = "English",
volume = "16",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI",
number = "6",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Robust statistical approaches for RSS-based floor detection in indoor localization

AU - Razavi, Alireza

AU - Valkama, Mikko

AU - Lohan, Elena Simona

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heardWiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

AB - Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heardWiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

KW - Floor detection

KW - Indoor localization

KW - Robust regression

KW - RSS-based localization

KW - Trilateration

KW - Weighted centroid localization

U2 - 10.3390/s16060793

DO - 10.3390/s16060793

M3 - Article

VL - 16

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 6

M1 - 793

ER -