SPRING+: Smartphone Positioning from a Single WiFi Access Point

Abstract

Indoor positioning is a major challenge for location-based services. WiFi deployments are often used to address indoor positioning. Yet, they require multiple access points, which may not be available or accessible for localization in all scenarios, or they make unrealistic assumptions for practical deployments. In this paper we present SPRING+, a positioning system that extracts and processes Channel State Information (CSI) and Fine Time Measurements (FTM) from a single Access Point (AP) to localize commercial smartphones. First, we propose an adaptive method for estimating the Angle of Arrival (AOA) from CSI that works on single packets and leverages information from the estimated number of paths. Second, we present a new method to detect the first path using FTM measurements, robust to multipath scenarios. We evaluate SPRING+ in an extensive experimental campaign consisting of four different testbeds: i) generic indoor spaces, ii) generic indoor spaces with obstacles, iii) office environments and iv) home environments. Our results show that SPRING+ is able to achieve a median 2D positioning error between 1 and 1.8 meters with a single WiFi AP.