| Popis: |
Location based services are becoming abundant and more reliable in today’s world thanks to the technological advancements achieved in the fields of positioning, navigation, and timing. Indoor asset tracking is an essential element of smart automation, warehousing, and manufacturing in industrial environments. Accurate indoor positioning systems (IPSs) exist with heavy financial costs depending on the degree of integrity required, consequently, numerous wireless based systems can be regarded as economical solutions. However, wireless positioning technologies suffer deep channel impairments especially in dense indoor venues that comprise various metallic and concrete structures. In this article, we showcase our work-in-progress research that studies a dense industrial environment in the context of indoor asset tracking. We experiment three potential wireless technologies: Ultra wideband (UWB), Bluetooth low energy (BLE) and Wi-Fi, to render a comparative assessment. Using a Multi-sensor fusion approach, we tend to complement the flaws in one technology with the merits of another, aided by physical quantity sensors like inertial motion units (IMUs). Moreover, we developed a machine learning optimization model to improve the results of the fusion based positioning scheme. The results are to be verified against millimeter-accurate reference measurements, then a seamless positioning scheme for indoor asset tracking can be achieved. |
