Exploiting Channel Correlations for NLOS ToA Localization with Multivariate Gaussian Mixture Models

In this paper, we develop a Bayesian probabilistic approach for time-of-arrival (ToA) localization in non-line-of- sight (NLOS) channels, where multivariate Gaussian mixture models (GMM) are used to approximate the joint distribution of channel bias values and harness the channel correlations. Also, in our experiment, using over-the-air measurements from a proprietary localization system, we numerically demonstrate that the proposed algorithm outperforms both a counterpart Bayesian probabilistic approach that does not take into account channel correlations and the well-known non-linear least square (NLS) optimization method.