Multi-Fingerprint for Wireless Localization in Time-Varying Indoor Environment

Fingerprint is one of the representative methods for wireless indoor localization. It uses a fingerprint database (measured in the offline phase) and the current received signal strengths (RSSs) (measured by the user's device in the online phase) to determine the location of this device. However, the RSSs and hence the localization accuracy would be affected by time-varying environmental factors (e.g., number of people in a shopping mall). In this paper, we propose a new method for wireless localization in time-varying indoor environments. In the offline phase, the proposed method measures extra information: it measures $E$ fingerprint databases for $E$ respective environmental conditions, where $E$ is a design parameter (e.g., $E=2$ for the peak period and the non-peak period in a shopping mall). In the online phase, it leverages the extra information for better localization in time-varying indoor environment, even when the current environmental condition is different from the ones considered in the offline phase. The proposed method is particularly suitable for the indoor venues for which their primary concern is to provide good quality localization services while they could afford a moderate amount of extra resources for one-off measurement in the offline phase (e.g., exhibition centers, airports, shopping malls, etc.). We conduct a simulation experiment and a real-world experiment to demonstrate that the proposed method gives accurate localization.