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Location fingerprinting based on RSSI becomes a mainstream indoorlocalization technique due to its advantage of not requiring the installationof new infrastructure and the modification of existing devices, especiallygiven the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi accessin modern buildings. The use of AI/ML technologies like DNNs makes locationfingerprinting more accurate and reliable, especially for large-scalemulti-building and multi-floor indoor localization. The application of DNNs forindoor localization, however, depends on a large amount of preprocessed anddeliberately-labeled data for their training. Considering the difficulty of thedata collection in an indoor environment, especially under the current epidemicsituation of COVID-19, we investigate three different methods of RSSI dataaugmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a singlefloor, by neighboring floors, and by a single building; unlike Single-OutputGaussian Process (SOGP), MOGP can take into account the correlation among RSSIobservations from multiple Access Points (APs) deployed closely to each other(e.g., APs on the same floor of a building) by collectively handling them. Thefeasibility of the MOGP-based RSSI data augmentation is demonstrated throughexperiments based on the state-of-the-art RNN indoor localization model and theUJIIndoorLoc, i.e., the most popular publicly-available multi-building andmulti-floor indoor localization database, where the RNN model trained with theUJIIndoorLoc database augmented by using the whole RSSI data of a building infitting an MOGP model (i.e., by a single building) outperforms the other twoaugmentation methods as well as the RNN model trained with the originalUJIIndoorLoc database, resulting in the mean three-dimensional positioningerror of 8.42 m.

Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization