Employing Genetic Algorithm and Particle Filtering as an Alternative for Indoor Device Positioning

Radio signals may contribute to seamless interactions with physical objects providing means to guide users from their position to a particular object within a room or store for instance. To achieve such a goal, a mechanism is needed to allow users to identify and locate objects of interest. Trilater...

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Bibliographic Details
Published in:	2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) pp. 1 - 7
Main Authors:	David Branco, Guilherme, Bordim, Jacir Luiz
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2018
Subjects:	Distance Estimation Estimation Genetic algorithms Indoor Positioning Kalman filters KalmanFilter Mathematical model Particle Filtering Particle filters Transmitters Trilateration
Online Access:	Get full text
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Summary:	Radio signals may contribute to seamless interactions with physical objects providing means to guide users from their position to a particular object within a room or store for instance. To achieve such a goal, a mechanism is needed to allow users to identify and locate objects of interest. Trilateration, fingerprinting and particle filter are usually employed as mechanisms for position estimation in indoor environments. This paper explores the the use of Genetic Algorithms (GA) combined with Particle Filter (PF) mechanism as an alternative to estimate indoor object position. The proposed scheme, named EPF (Evolutionary Particle Filter) has been compared to particle filter and trilateration. Simulation results show that the proposed EPF improves positioning accuracy by 1.5 cm (10%) and 30 cm (300%) over particle filter and trilateration, respectively.
DOI:	10.1109/CANDARW.2018.00009