Fusion particle and fingerprint recognition for indoor positioning system on mobile

Abstract

High accuracy localization is easily obtained in an exterior context with GPS, but the development of indoor positioning systems remains a challenge. The GPS signals, by nature, cannot penetrate walls thus preventing this technology to provide any service indoor. This paper proposes a practical implementation of an accurate Indoor Positioning System based on Bluetooth Low Energy technology to ensure a low power, efficient and easy to setup infrastructure. It is available on any current Smartphone and requires no extra devices for the user. We use a fusion of the inertial sensors available on the device to output a precise and drift-free estimation of the user displacement, leverage this information with iBeacon radio signals used as anchors to readjust the path if needed and process all these inputs in a Particle Filter. We augmented the Fingerprinting-based likelihood calculation of the particles position’s with a unique simulation of the particle’s theoretical RSSI and also reduced the number of particles to better fit within the bounds of the computational capabilities of mobile devices. The experiment was conducted in a 400 m2 open space and yielded positive results as a first attempt in accurate indoor localization and proved to be viable in an indoor context.