Iterative Bayesian-based Localization Mechanism for Industry Verticals

We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target’s coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

Authors:
Hilleshein Henrique, de Lima Carlos H. M., Alves Hirley, Latva-aho Matti

Publication type:
A4 Article in conference proceedings

Place of publication:
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

Keywords:
6G Publication

Published:
30 June 2020

Full citation:
H. Hilleshein, H. M. Carlos de Lima, H. Alves and M. Latva-aho, “Iterative Bayesian-based Localization Mechanism for Industry Verticals,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5, doi: 10.1109/VTC2020-Spring48590.2020.9128442

DOI:
https://doi.org/10.1109/VTC2020-Spring48590.2020.9128442

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020081248381