Variable-Weighted Error Propagation Model of a Ultra-Wide-Band Indoor Positioning System in an Intelligent Manufacturing Lab

Abstract

Ultra-wide-band (UWB) positioning is a satisfying indoor positioning technology with high accuracy, low transmission cost, high speed, and strong penetration capacity. However, there remains a lack of systematic study on inevitable and stochastic errors caused by factors originating from the multipath effect (ME), non-line-of-sight interference (NLOSI), and atmospheric interference (AI) in UWB indoor positioning systems. To address this technical issue, this study establishes a dynamic error-propagation model (DEPM) by mainly considering the ME, NLOSI, and AI. First, we analyze the UWB-signal generation principle and spread characteristics used in indoor positioning scenarios. Second, quantization models of the ME, NLOSI, and AI error factors are proposed based on data from related studies. Third, to adapt to various environments, we present a variable-weighted DEPM based on the quantization models above. Finally, to validate the proposed dynamic error-propagation model, UWB-based positioning experiments in an intelligent manufacturing lab were designed and conducted in the form of static and dynamic longitude-tag position measurements. The experimental results showed that the main influencing factors were ME and NLOSI, with a weight coefficient of 0.975, and AI, with a weight coefficient of 0.00025. This study proposes a quantization approach to main error factors to enhance the accuracy and precision of indoor UWB-positioning systems used in intelligent manufacturing areas.