Indoor Location Tracking Using Inertial Navigation Sensors and Radio Beacons

Abstract

We consider the problem of real-time sensing and tracking the location of a moving cart in an indoor environment. To this end, we propose to combine position information obtained from an inertial navigation system (INS) and a short-range wireless reference system that can be embedded into a future "network of things". The data produced by the INS lead to accurate short-term position estimates, but due to the drifts inherent to the system, these estimates perform loosely after some time. To solve this problem, we also generate estimates with a wireless reference system. These radio-based estimates can be used as accurate long-term position estimates because their accuracy improves over time as the channel fading can be averaged out. We use a data fusion algorithm based on Kalman filtering with forward/backward smoothing to optimally combine the short- and long-term position estimates. We have implemented this localization system in a real-time testbed. The measurement results, which we obtained using the proposed method, show considerable improvements in accuracy of the location estimates.