Bayesian filters for ToF and RSS measurements for indoor positioning of a mobile object

Author

Galov, Aleksandr ; Moschevikin, Alex

Author_Institution

RTL-Service Ltd., Petrozavodsk State Univ., Petrozavodsk, Russia

fYear

2013

fDate

28-31 Oct. 2013

Firstpage

1

Lastpage

8

Abstract

The article describes three different types of Bayesian filtering methods (Kalman filter, particle filter, and grid-based filter) applied for indoor localization in NanoLOC (IEEE 802.15.4a) wireless sensors network. Received Signal Strength, Time-of-flight measurements and the building structure were used for position calculations. The comparison of the applied algorithms revealed conditions at which one algorithm is superior to others. The techniques described in this paper are not depending on the used RF technology.

Keywords

Bayes methods; Kalman filters; indoor radio; particle filtering (numerical methods); sensor placement; wireless sensor networks; Bayesian filter; IEEE 802.15.4a; Kalman filter; NanoLOC; RSS measurement; ToF measurement; grid based filter; indoor positioning; mobile object; particle filter; position calculation; received signal strength; time-of-flight methods; wireless sensor network; Atmospheric measurements; Equations; Kalman filters; Mathematical model; Noise measurement; Particle measurements; Vectors; Bayesian filtering; NanoLOC; RSS; ToF; grid-based filter; indoor positioning; particle filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on

Conference_Location

Montbeliard-Belfort

Type

conf

DOI

10.1109/IPIN.2013.6817845

Filename

6817845