A comparison of Bayesian localization methods in the presence of outliers
Paper in proceeding, 2017

Localization of a user in a wireless network is challenging in the presence of malfunctioning or malicious reference nodes, since if they are not accounted for, large localization errors can ensue. We evaluate three Bayesian methods to statistically identify outliers during localization: an exact method, an expectation maximization (EM) method proposed earlier, and a new method based on Variational Bayesian EM (VBEM). Simulation results indicate similar performance for the latter two schemes, with the VBEM algorithm able to provide a statistical description of the user location, rather than an estimate as in the simpler EM case. In contrast to previous studies, we find that there is a significant gap between the approximate methods and the exact method, the cause of which is discussed.

Bayesian methods

Wireless networks




N.G. Ferrara

Tampere University of Technology

Henk Wymeersch

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

E.S. Lohan

Tampere University of Technology

Jari Nurmi

Tampere University of Technology

2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017


Multi-technology positioning professionals (MULTI-POS)

European Commission (EC) (EC/FP7/316528), 2012-10-01 -- 2016-09-30.

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