Indoor localization via WLAN path-loss models and Dempster-Shafer combining

In this paper, in order to improve the accuracy of mobile user location estimation, we investigate a new approach based on path-loss algorithms with non-Bayesian data fusion based on Dempster-Shafer Theory (DST). Traditionally, Bayesian framework is used in Wireless Local Area Network (WLAN) positioning. Nevertheless, alternative approaches such as DST have also good potential in WLAN positioning, as it has been previously shown by using DST with WLAN fingerprinting. Our paper focuses on Path-Loss (PL) probabilistic approaches, which have the advantage of a lower number of parameters and lower implementation complexity compared with the fingerprinting approaches. We combine, for the first time in the literature, the PL position estimators with DST. PL approaches can be implemented with a variety of algorithms, and the deconvolution algorithms used in our paper are among the most promising implementations, due to their simplicity. We study the performance of the PL approaches with real-field data measurements and we show that the DST can increase the floor detection probability and decrease the distance Root Mean Square Error (RMSE) compared to the approaches using Bayesian combining.