# Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking
## 2 Literature Review
### Ultra-WideBand Technology  
UWB technology has spread widely in the research community in recent years; it is used for both outdoor and indoor localization systems. UWB is a radio technology that has low energy consumption for short-range, high-bandwidth communications over a large portion of the radio spectrum. State-of-the-art UWB applications focus on sensor data collection, precision locating, and tracking. A recent study highlights that the global uncertainty of RTLS location systems based on UWB technology when the tag position errors are not corrected is estimated to be 0.35 m [47]. At the laboratory level (TRL 4 technology validated in the lab), numerous UWB studies have analyzed the performance of UWB radio ranging and positioning systems. Numerous commercial UWB kits are available for research labs and engineering teams nowadays, such as Decawave (which was acquired by Qorvo [48]), BeSpoon (which was acquired by STMicroelectronics, Geneva, Switzerland), Ubisense [49], SEWIO [50], etc. A comparison between Decawave and BeSpoon UWB RTLSs in LOS and NLOS conditions concluded that Decawave has lower position ing error but at a slightly smaller range than BeSpoon in NLOS conditions. Ultimately, the overall difference in performance of these modules is negligible [51]. Furthermore, a comparative experiment between the Ubisense, BeSpoon and Decawave UWB modules was conducted [52]. The evaluation took place in an NLOS environment that simulates an industrial warehouse with diverse equipment (robots, vehicle, railways, etc. ), which perturbates UWB radio propagation and increases the positioning error. Under these conditions, the performance of the Decawave system was slightly better than that of BeSpoon and significantly more reliable than the Ubisense system. A multi-channel UWB system was proposed, and it was concluded that it was able to reduce the RMSE by 0.1 m compared to single-channel UWB systems [53]. Another comparison between the cheap DWM3000 EVB and the expensive OptiTrack system [54] was executed without significant differences in localization accuracy [55]. Additionally, a custom UWB system based on a dynamic algorithm was proposed; it was able to localize assets with an error lower than 10 cm [56]. The minimal localization error enabled the application of UWB RTLS systems in more demanding scenarios, such as UAV landing assistance systems [57]. Finally, a novel collaborative localization method for UWB massive multiple input multiple output (MIMO) systems, named UWB-CollLoc, was proposed. This method addressed the challenges of anchor placement, communication reliability, and NLOS issues, employing Smart Crow Search Optimization (SCSO) for the anchor placement, a lightweight attention network (LAN) for identifying LOS and NLOS paths, and a semi-distributed approach combining the time difference of arrival (TDoA) and angle of arrival (AoA) for accurate localization. The experimental results demonstrate superior performance in terms of localization accuracy, efficiency, and reduced deployment costs compared to existing approaches [58]. The recent years, more sophisticated methods to increase the localization accuracy and minimize the NLOS impact have been proposed. A recent study evaluated the localization accuracy of a UWB kit using the DWM1000 module with 0, 1, 2, 3, and 4 biased anchors, resulting in 2D position accuracies of 0.05 m, 0.09 m, 0.15 m, 0.58 m, and 0.49 m, respectively; this shows that even one biased NLOS range affects the localization accuracy dramatically [59]. Similarly, a study utilizing generative adversarial networks (GANs) for data augmentation and a convolutional neural network (CNN) for LOS/NLOS classification demonstrated an average positioning error of 0.54 m [60]. A denoising neural network for enhanced UWB ranging was proposed to predict the ranging error.