Finer-level Sequential WiFi-based Indoor Localization

Abstract

The WiFi-based indoor localization problem aims to identify the location of a user using the signals received from surrounding wireless access points. A major approach to address this problem is through machine learning algorithms trained on precollected radio maps. However, these approaches either completely ignore the temporal aspects of the problem or the interval between consecutive reference points is too large. Therefore, in this work, we study the application of end-to-end sequence models for finer-level WiFi-based indoor localization. We show that localization task can be formulated as a sequence learning problem by using recurrent neural networks with regression output. The regression output is used to estimate three-dimensional positions and allows the network to easily scale to larger areas. In addition, we present our WiFine dataset containing 290 trajectories sequentially collected at finer-level reference points. The dataset is made publicly available for advancing sequential indoor localization research. The experiments performed on WiFine dataset show that on finer-level localization task the recurrent neural networks are superior to non-sequential models such as k-nearest neighbors and feedforward neural network.