Seminár Ústavu informatiky


Indoor Localization using Bayesian Filtering

Mgr. Miroslav Opiela
Ústav informatiky, PF UPJŠ

18. októbra 2017 (streda) o 12:45
SJ2S07 (3.16T), PF UPJŠ


The indoor localization problem is considered to be a time-sequential, non-linear and non-Gaussian state estimation problem. Bayesian filtering is the commonly used stochastic filtering techique for the indoor positioning, where the current state of a system is computed from its previous state based on observations (in this case obtained from the smartphone sensors).

Under some assumption, Kalman filter and grid-based method provide the optimal solution for the posterior probability density computation. In other cases, including indoor positioning, Kalman filter, particle filter and grid-based method only approximate the posterior density. We will explain these Bayes filters when applied on the indoor positioning problem and provide evaluation results for the grid-based approach focusing on the influence of using the discrete filter and the localization accuracy for different grid types and step length estimations.