Hybrid Bayesian-based indoor localization mechanisms for distributed antenna systems

This work proposes and evaluates a hybrid Bayesian-based localization method to estimate the position of a target node using received signal strength and time of flight measurements. In our investigations, we consider these measurements are acquired through a distributed antenna system which is connected to a common master anchor node. The baseline non-hybrid scenarios use only received signal strength measurements to estimate the position of interest, while the hybrid implementation combines time of arrival measurements as well. Both Bayesian-based (non) hierarchical approaches approximates the posterior distribution of the target’s location coordinates using Markov Chain Monte Carlo methods. The hierarchical method introduces conditional interdependencies to the model parameters, resulting in less model variance. Herein, the root mean square error is used to evaluate the performance of the indoor test scenarios. Our results show that both hybrid and hierarchical approaches outperform the baseline Bayesian model, while the former significantly increase the accuracy the target position estimate.