Neural 5G Indoor Localization with IMU Supervision
Aleksandr Ermolov Shreya Kadambi Maximilian Arnold Mohammed Hirzallah Roohollah Amiri Deepak Singh Mahendar Singh Srinivas Yerramalli Daniel Dijkman Fatih Porikli Taesang Yoo Bence Major
Radio signals are well suited for user localization because they are
ubiquitous, can operate in the dark and maintain privacy. Many prior works
learn mappings between channel state information (CSI) and position
fully-supervised. However, that approach relies on position labels which are
very expensive to acquire. In this work, this requirement is relaxed by using
pseudo-labels during deployment, which are calculated from an inertial
measurement unit (IMU). We propose practical algorithms for IMU double
integration and training of the localization system. We show decimeter-level
accuracy on simulated and challenging real data of 5G measurements. Our
IMU-supervised method performs similarly to fully-supervised, but requires much
less effort to deploy.