Enabling Temporal Variation Resilience for ML-Based Indoor Localization

Abstract

Wi-Fi fingerprint-based localization is known to be prominent for indoor positioning technology; however, it is still challenging on sustainability of its performance for long-term use due to distribution drifts of the signal strength across time. Therefore, the laborious continual surveys on fingerprint and periodic model recalibration are inevitable. Since the cost of maintaining common Wi-Fi fingerprint-based localization against age deterioration of the fingerprints is very high, techniques retaining the model are investigated. This chapter explains these related methods including transfer learning and sampling; a small but sufficient set of additional supervised datasets are investigated.