Low-Memory Indoor Positioning System for Standalone Embedded Hardware

Abstract

As the proportion and importance of the indoor spaces in daily life are gradually increasing, spatial information and personal location information become more important in indoor spaces. In order to apply indoor positioning technologies in any places and for any targets inexpensively and easily, the system should utilize simple sensors and devices. In addition, due to the scalability, it is necessary to perform indoor positioning algorithms on the device itself, not on the server. In this paper, we construct standalone embedded hardware for performing the indoor positioning algorithm. We use the geomagnetic field for indoor localization, which does not require the installation of infrastructure and has more stable signal strength than RF RSS. In addition, we propose low-memory schemes based on the characteristics of the geomagnetic sensor measurement and convergence of the target's estimated positions in order to implement indoor positioning algorithm to the hardware. We evaluate the performance in two testbeds: Hana Square (about 94 m x 26 m) and SK Future Hall (about 60 m x 38 m) indoor testbeds. We can reduce flash memory usage to 16.3% and 6.58% for each testbed and SRAM usage to 8.78% and 23.53% for each testbed with comparable localization accuracy to the system based on smart devices without low-memory schemes.