The advent of the fourth industrial revolution (Industry 4.0) aims at increasing automation and efficiency in manufacturing processes by the adoption of information and communication technologies. Several of the proposed solutions rely on precise localization of material, equipment, or operators. This article investigates the employment of ultrawideband (UWB) real-time location systems (RTLS) in a factory environment and proposes an augmentation technique to mitigate the impairments that arise in such a complex scenario. A Bayesian filtering method is developed to jointly track the motion dynamics and the time-varying visibility conditions of the UWB antennas, with particle-based implementation to deal with the nonlinearity of the UWB measurements. Laboratory tests and industrial experiments are carried out to evaluate the performance of three commercial off-the-shelf UWB technologies: Decawave, Sewio, and Ubisense. The experimental data are then used to calibrate and test the developed filtering technique, showing that it is possible to significantly reduce the positioning error originating from dense multipath and NLOS effects by jointly tracking the target dynamics and visibility conditions.

中文翻译：

---

智能工厂中的 UWB 定位：增强方法和实验评估


第四次工业革命（工业4.0）的到来旨在通过采用信息和通信技术来提高制造过程的自动化和效率。所提出的一些解决方案依赖于材料、设备或操作员的精确定位。本文研究了超宽带 (UWB) 实时定位系统 (RTLS) 在工厂环境中的应用，并提出了一种增强技术来减轻这种复杂场景中出现的损害。开发了贝叶斯滤波方法来联合跟踪 UWB 天线的运动动态和随时间变化的可见性条件，并使用基于粒子的实现来处理 UWB 测量的非线性。我们进行了实验室测试和工业实验，以评估三种商用现成 UWB 技术的性能：Decawave、Sewio 和 Ubisense。然后用实验数据来校准和测试所开发的滤波技术，结果表明，通过联合跟踪目标动态和能见度条件，可以显着减少密集多径和非视距效应引起的定位误差。