A regularization framework for fingerprint-based reconstruction of mobile trajectories

In many systems, due to the lack of an adequate positioning capability or the need for energy saving, it is infeasible to track the location of a mobile device as it is moving. Its trajectory, however, may be reconstructed from the real-time fingerprint data that are obtained by the sensors built in the device. For this purpose, we investigate a regularization framework aimed to maximize the localization accuracy by taking into account the spatiotemporal properties regarding the fingerprint space in relation to the location space. The viability of this framework is demonstrated in an evaluation using real-world datasets, which shows its potential to outperform conventional approaches to location fingerprinting.