BBAuthors: Fan, J.; Advisor: -; Participants: Awan, A. S. (2019)
Identi cation of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband localization systems. In the identi cation, supervised machine learning is often used, but it requires exorbitant efforts to maintain and label the LOS and NLOS database. In this paper, we apply unsupervised machine learning approach called ``expectation maximization for Gaussian mixture models'' to classify LOS and NLOS components. The key advantage of applying unsupervised machine learning is that it does not require any rigorous and explicit labeling of the database at a certain location. The simulation results demonstrate that by using the proposed algorithm, L... 
