A WiFi Fingerprint Augmentation Method for 3-D Crowdsourced Indoor Positioning Systems

WiFi received signal strength (RSS)-based finger-printing has attracted much attention in indoor positioning in the past decade. One WiFi fingerprint comprises multiple RSS values annotated with the location (reference point) where the signals are obtained. The positioning accuracy of WiFi RSS fingerprinting-based indoor positioning systems is highly reliant on the data volume of the observed signals. However, taking such data in a large complex indoor area is usually time-consuming and labor-intensive. In recent years, crowdsourcing approaches have been proposed to collect WiFi data and record the location by utilizing the trajectories of common users to reduce the burden of constructing the database. Nevertheless, crowdsourced data is usually sensitive to crowd density. The data coverage is usually not enough to cover the entire targeted environment to provide good positioning accuracy, particularly at the beginning stage of constructing a database. Besides, it is also expected that some regions do not have enough fingerprints to provide good positioning performance since they do not have as many visitors as others. Therefore, this paper proposes a WiFi fingerprint augmentation method to generate more fingerprints by predicting RSS values on unsurveyed locations through a multivariate Gaussian process regression (MGPR) model. Evaluations are conducted on an open-source crowdsourced WiFi fingerprint dataset collected in an actual multi-floor university building. The experiment results show that the proposed WiFi fingerprint augmentation method can enhance the global data coverage (considering the entire building) to reduce the positioning error by 5% to 20%. Also, the proposed method can sharply reduce the positioning error in some indoor regions by improving local data density (considering a 2D region on a certain floor).