  Location fingerprinting based on RSSI becomes a mainstream indoor
localization technique due to its advantage of not requiring the installation
of new infrastructure and the modification of existing devices, especially
given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access
in modern buildings. The use of AI/ML technologies like DNNs makes location
fingerprinting more accurate and reliable, especially for large-scale
multi-building and multi-floor indoor localization. The application of DNNs for
indoor localization, however, depends on a large amount of preprocessed and
deliberately-labeled data for their training. Considering the difficulty of the
data collection in an indoor environment, especially under the current epidemic
situation of COVID-19, we investigate three different methods of RSSI data
augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single
floor, by neighboring floors, and by a single building; unlike Single-Output
Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI
observations from multiple Access Points (APs) deployed closely to each other
(e.g., APs on the same floor of a building) by collectively handling them. The
feasibility of the MOGP-based RSSI data augmentation is demonstrated through
experiments based on the state-of-the-art RNN indoor localization model and the
UJIIndoorLoc, i.e., the most popular publicly-available multi-building and
multi-floor indoor localization database, where the RNN model trained with the
UJIIndoorLoc database augmented by using the whole RSSI data of a building in
fitting an MOGP model (i.e., by a single building) outperforms the other two
augmentation methods as well as the RNN model trained with the original
UJIIndoorLoc database, resulting in the mean three-dimensional positioning
error of 8.42 m.
