## 1 Introduction  
There are many methods of UWB positioning technology. This paper considers only the ranging principle based on time of flight (TOF) [1], which is one of the most common positioning methods in the UWB positioning method. TOF-ranging technology is a bidirectional-ranging technology. It calculates the flight time of the signal in the two modules, and then multiplies the distance between the two modules by the speed of light. The distance has an acceptable error and can result in satisfactory accuracy. Besides the distance, many objects in the environment will reflect the signals and affect the position accuracy. Different from line-of-sight (LOS) signals, signal transmission path delays contained in non-line-of-sight (NLOS) signals result in a positive range and position errors. Therefore, it is important to carry out the LOS/NLOS classification and NLOS mitigation before using ranging information from the channel to calculate the position [2, 3]. Refs [4, 5] conducted NLOS identification and mitigation for the position based on the UWB experimental data. In addition, as in traditional research, features extracted from the UWB channel impulse response (CIR) or some other signal properties would be used as input vectors for common machine learning methods. Different from common machine learning methods, [PERSON] et al. used a convolutional neural network (CNN) [6], and [PERSON] et al. used a novel convolutional neural network (CNN)-long short-term memory (LSTM) for the UWB NLOS/LOS signal classification and obtained the state-of-the-art classification performance [7]. Apart from the above machine learning methods, [PERSON] et al. proposed a semi-supervised learning approach for UWB ranging error mitigation, and the decrease in the ranging error was significant [8]. In the application of indoor positioning, UWB technology can achieve centimeter-level positioning accuracy (generally referred to as 2-dimensional plane positioning) and has good anti-multipath interference, weakening performance, and strong penetration ability [9; 10; 11; 12; 13; 14]. However, due to the complex and changeable indoor environment, UWB communication signals are easily blocked. Although UWB technology has a penetrating ability, it will still generate errors. If strong interference exists, the data will fluctuate abnormally (usually time delay) and even cause serious errors [15; 16; 17; 18]. Therefore, the problem of UWB precise positioning under signal interference has become an urgent problem to be solved [19]. Based on the above background, the research tasks of this paper can be shown as follows:  
Task 1: Data Preprocessing (Cleaning)  
Whether it is collecting data with or without signal interference, the Tag will collect multiple sets of data at the same coordinate point (648 data files in annex 1), capture the corresponding values of each data file, and capture the corresponding values of each data file. Convert into a two-dimensional table (matrix) form (\"txt\", \"Excel\" or other data format), each row represents a set of data, and then preprocess (clean) these data files to remove some \"useless\" (abnormal, missing, identical, or similar) data. Task 2: Positioning the Model  
Use the data processed in task 1, design appropriate the mathematical models (or algorithms) for \"normal data\" and \"abnormal data,\" estimate (or predict) the exact position of the Tag, and explain the established positioning model (or Algorithm); at the same time, use the positioning model (or algorithm) to perform the precise positioning (3-dimensional coordinates) for the first 5 groups of (no signal interference) data and the last 5 groups of (signal interference) data provided in annex 2. Where (1) the positioning model will reflect the experimental scene information. (2) The 3-dimensional (x, y, z) accuracy, 2-dimensional (x, y) accuracy, and 1-dimensional accuracy of the positioning model will be given.