Practical Indoor Localization via Smartphone Sensor Data Fusion Techniques: A Performance Study

Accurate indoor localization constitutes a challenging yet fundamental research problem towards the large-scale deployment of next-generation mobile indoor location-based services. This paper addresses two key issues of indoor localization: (i) how to take benefit of the presence of inertial sensors, short-range and long-range radio interfaces on modern smartphones in order to achieve fine-grained localization and trajectory tracking, and-at the same time-(ii) how to perform it while limiting the impact on energy-constrained devices. To address the first issue, we propose a novel hybrid strategy which implements a dual-step fusion process, i.e., it merges the estimations produced by pattern matching algorithms applied to short-range and long-range wireless sources available on smartphones and then it merges the estimations produced by Pedestrian Dead Reckoning (PDR) and Radio Fingerprinting (RF) techniques, in order to overcome the limitations of each approach. For the second issue, we describe the design and implementation of a novel client-server architecture, which offloads the computational expensive tasks to the infrastructure, while still guaranteeing acceptable localization lag. Finally, a modular, extensive evaluation is proposed on real-world scenarios, quantifying the impact of each sensor/source on the localization accuracy, and the gain induced by the dual-step fusion process over basic PDR localization techniques.