Localization error modeling of hybrid fingerprint-based techniques for indoor ultra-wideband systems

Abstract

Acquiring an accurate estimate of a device's indoor position can lead to the development of various services, such as disaster recovery, content delivery and efficient point-to-point communications. To this end, different positioning techniques have been developed aiming to minimize the localization error. UWB signals are well-suited to communication and tracking systems, as they provide increased tolerance to localization errors due to the fine time resolution of the transmitted pulses and their large bandwidth. This work presents two indoor Ultra-Wideband (UWB) positioning schemes based on the hybrid combination of Fingerprinting with temporal or angular data. The proposed schemes are evaluated using five different propagation models and different numbers of Anchor Nodes (AN) that emit omnidirectional UWB signals. Then, measures are gathered for these parameters and results are presented in terms of localization and angular error. Also, results are provided for the basic statistics, cumulative distribution function and the boxplot of the positioning error, thus providing probabilistic modeling for the localization error. The performance evaluation shows that the two proposed hybrid UWB schemes provide accurate indoor positioning independently of the type of data used during the localization procedure and optimum placement along with the optimum number of the ANs is provided.