CRIL: An Efficient Online Adaptive Indoor Localization System

Abstract

Indoor localization or indoor positioning systems find their use in many important applications such as augmented reality, guided tours, tracking and monitoring, and situational awareness and have recently attracted intense research interests. Previous localization systems are usually received signal strength indication (RSSI)-based, inertial navigation system (INS)-based, or an integration of these two. However, few of them can account for dynamic communication environments, where channel states constantly change. To the best of our knowledge, this paper is the first to propose an efficient and adaptive indoor localization system called coupled RSSI and INS localization (CRIL), which can adapt to dynamic communication environments quickly and effectively. Moreover, CRIL can account for the uncertainties in RSSI measurements such as varying covariances and outliers as well. Extensive simulation results demonstrate that our proposed CRIL system is able to track both slow changes and sudden changes of the channel states in dynamic environments. Noticeably, the proposed CRIL can perform accurate localization with estimation errors up to $\text{1} \, \mathrm{m}$ , while previous schemes’ localization errors are up to several meters or even tens of meters. Moreover, we test CRIL in real experiments, and its localization error is up to 3 m in dynamic environments.