Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments: Supplementary Materials
Joaquín Torres-Sospedra, Adriano J. C. Moreira, Germán Martín Mendoza-Silva, Maria João Nicolau, Miguel Matey-Sanz, Ivo Silva, Joaquı́n Huerta, Cristiano Pendão · Figshare · 2019
Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This package includes the data sets and software (MatLab) required to select the best model based on both approaches through a simulated environment. Citation Request Torres-Sospedra, J.; Moreira, A.; Mendoza-Silva, G. M.; Nicolau, M. J.; Matey-Sanz, M.; Silva, I.; Huerta, J.; and Pendão, C. Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2019. Torres-Sospedra, J.; Moreira, A.; Mendoza-Silva, G. M.; Nicolau, M. J.;Matey-Sanz, M.; Silva, I.; Huerta, J.; and Pendão, C. Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments: Supplementary Materials, Zenodo 2019. http://dx.doi.org/10.5281/zenodo.3333466