A wireless LAN-based indoor positioning technology

TLDR

Context‑aware computing is an emerging paradigm that enhances services by exploiting user context information. This study introduces a WLAN‑based indoor positioning system, proposing a model‑based signal‑distribution training scheme to balance accuracy and workload, and a tracking‑assistant algorithm that leverages area topology. The system deploys a position‑determination model that gathers WLAN signal data, applies the training scheme, and uses the tracking‑assistant algorithm, and was implemented at IBM China Research Laboratory. Experimental results demonstrate 2‑m accuracy for static devices and 5‑m for walking devices at 90% probability, while significantly reducing training complexity relative to existing methods.

Abstract

Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms.