Switching Model Stein Variational Sampling Filter for Mixed LOS/NLOS Industrial Indoor Positioning

Abstract

Internet of Things wireless technologies serve as key enabler for location-based services in emerging applications, such as autonomous robotics, industrial automation, augmented reality, and virtual reality. Wideband technologies, including ultra wideband (UWB) and 5G-advanced millimeter-waves, are the preferred solutions in these contexts for their high potentials in precise positioning. A main challenge is the mitigation of radio propagation effects that arise in complex environments, such as in industrial facilities, where frequent blockage events limit the accuracy and integrity of localization services. This article tackles the problem focusing on precise indoor navigation in industrial environments with dense and dynamic blockage conditions. Our proposal relies on an innovative particle filtering technique, based on the Stein variational adaptive importance sampling, to improve the sampled representation of the location posterior distribution by integrating prior information on the intermittent visibility-blockage dynamics. We assess the proposed solution through indoor experiments conducted in industrial scenarios using UWB devices. Our results show significant improvements with respect to state-of-the-art filters in terms of both accuracy and robustness of the location tracking.