Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

TLDR

Traditional planar‑map WiFi fingerprint systems suffer from high computational cost and poor performance, limiting their use on smartphones for indoor positioning. This study proposes an integrated 3D indoor positioning system that fully exploits smartphone hardware sensors. By combining an improved K‑means clustering for faster fingerprint lookup, an auto‑correlation based step‑counting method, WiFi‑PDR fusion via an Unscented Kalman Filter, and a Unity‑3D real‑time hybrid platform, the authors achieve efficient, accurate 3D localization.

Abstract

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.