In Industry 4.0, real-time location systems are emerging as a key technology to improve the efficiency of industrial processes, as they allow to track any assets or material movement and collect data on their usage. Ultra Wideband (UWB) systems offer unrivaled localization accuracy, but they call for augmentation strategies in environments with complex propagation conditions such as plants or factories with high density of scattering objects and obstructions. In this paper, we focus on Bayesian filtering techniques to counterbalance the detrimental effects induced by non line of sight and dense multipath in a smart factory scenario. We first conduct a set of experimental tests with commercial devices in an industrial facility of Pirelli Tyre S.p.A. located in Milan, Italy. We then use the collected data to design and test augmentation algorithms based on Extended Kalman Filter (EKF) and Particle Filter (PF), fusing Time Difference of Arrival (TDoA) and Angle of Arrival (AoA) signals. Experimental results show that, despite the harsh environment, accurate localization is possible by fusion of hybrid measurements and integration of prior information on the target dynamics and the industrial propagation environment.

UWB Real-Time Location Systems for Smart Factory: Augmentation Methods and Experiments

Barbieri, Luca;Brambilla, Mattia;Pitic, Razvan;Nicoli, Monica
2020-01-01

Abstract

In Industry 4.0, real-time location systems are emerging as a key technology to improve the efficiency of industrial processes, as they allow to track any assets or material movement and collect data on their usage. Ultra Wideband (UWB) systems offer unrivaled localization accuracy, but they call for augmentation strategies in environments with complex propagation conditions such as plants or factories with high density of scattering objects and obstructions. In this paper, we focus on Bayesian filtering techniques to counterbalance the detrimental effects induced by non line of sight and dense multipath in a smart factory scenario. We first conduct a set of experimental tests with commercial devices in an industrial facility of Pirelli Tyre S.p.A. located in Milan, Italy. We then use the collected data to design and test augmentation algorithms based on Extended Kalman Filter (EKF) and Particle Filter (PF), fusing Time Difference of Arrival (TDoA) and Angle of Arrival (AoA) signals. Experimental results show that, despite the harsh environment, accurate localization is possible by fusion of hybrid measurements and integration of prior information on the target dynamics and the industrial propagation environment.
2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications
978-1-7281-4490-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1148849
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