UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment
UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment
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DOI：
10.1109/tim.2021.3074403
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发表时间：
2021-01-01
影响因子：
5.6
通讯作者：
Nicoli, Monica
中科院分区：
文献类型：
--
作者：
Barbieri, Luca;Brambilla, Mattia;Nicoli, Monica
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。所提出的几种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）在工厂环境中的就业，并提出了一种增强技术，以减轻在这样一个复杂的情况下出现的损害。贝叶斯滤波方法的开发，以联合跟踪运动动力学和随时间变化的UWB天线的可见性条件，与粒子为基础的实施，以处理UWB测量的非线性。进行了实验室测试和工业实验，以评估三个商业现成的UWB技术：Decawave，Sewio和Ubisense的性能。实验数据，然后使用校准和测试开发的滤波技术，表明它是可能的，以显着减少定位误差源于密集的多径和NLOS效应，通过联合跟踪目标的动态和能见度条件。
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.