Title :
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
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;
Conference_Titel :
Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on
Conference_Location :
Montbeliard-Belfort
DOI :
10.1109/IPIN.2013.6817845
