## 1 Introduction  
In general, positioning information can be provided by a global navigation satellite system (GNSS) [1] in outdoor usage for most moving objects. However, pure GNSSs cannot currently satisfy high-accuracy requirements in indoor positioning scenes because of the stochastic layout of shelters, obstacles, and the thick, metal roofs of workshops. In an indoor environment, different types of wireless positioning methods (e.g., Wi-Fi, ZigBee, and UWB) can be used to achieve relatively high-accuracy positioning. In practice, Wi-Fi positioning technology is realized in accordance with the intensity indication of signal reception [2]. Indoor positioning technology based on Wi-Fi signals has the advantages of a wide application range, low cost, and good portability [3]. However, external noise or other 2.4 GHz signals can easily interfere with Wi-Fi signals because of their limited transmission frequency bandwidth, leading to Wi-Fi positioning accuracy at the level of one meter. ZigBee wireless location technology was first introduced in 2002 [4]. However, the positioning accuracy based on ZigBee wireless positioning technology can reach only 1-5 m.  
UWB was originally developed as a military radar and was mainly used in the radar field during its early stages. In general, UWB wireless positioning technology uses pulse signals with extremely low power spectral density and narrow pulse width to transmit data, achieving high time resolution and strong barrier penetration. In a line-of-sight (LOS) environment, centimeter- or even millimeter-level of positioning accuracy can be obtained [5; 6; 7]. In UWB-based positioning, typical location algorithms are classified as time of arrival (TOA), time differential of arrival (TDOA) [8], time of flight (TOF), received signal strength (RSSI), and angle of arrival (AOA) [9; 10]. However, inevitable and stochastic errors are mainly caused by factors sourcing from the multipath effect (ME), non-line-of-sight interference (NLOSI), and atmospheric interference (AI) in UWB positioning systems. To address this technical issue, this study establishes a dynamic error-propagation model (DEPM) by mainly considering the ME, NLOSI, and AI. Positioning error challenges exist in current intelligent manufacturing workshops. The location and trajectory information of each production element in the intelligent manufacturing workshop is basic for transparent production and the intelligent manufacturing operation control center. When UWB indoor positioning is carried out in an intelligent manufacturing workshop, there are many factors affecting the positioning results: (1) equipment. The number and location of UWB positioning devices can affect their positioning accuracy. (2) Occlusion. This can occur during the propagation of UWB positioning signals between base stations, and tag, refraction, diffraction, and other phenomena can occur when they are blocked by indoor objects, which will change the amplitude and phase of the signal and then produce positioning errors. (3) Time. The crystal oscillator frequency of each base station usually has a certain deviation due to the manufacturing process and its use in the experiment. (4) Environment. The state of the atmosphere at different times (temperature, pressure, humidity, etc.) can affect the propagation speed of UWB signal waves. (5) Software delay. When the processor analyzes the obtained data information, the software can delay, causing poor real-time positioning. (6) Moving the object with the UWB tag. When the UWB positioning tag is fixed on a moving object, an AGV, or worn by a worker, it is inevitable that shaking occurs during movement, resulting in a deviation in signal transmission [11; 12]. The contributions of this study are as follows: (1) a dynamic propagation model of a UWB signal is established, and the factors that affect the UWB positioning accuracy are qualitatively analyzed.