# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
## 1 Introduction  
The concept of intelligent farming has received increasing attention and research in today's intensive, large-scale pig production environment. The use of machines over manual labor has emerged as the prevailing trend for efficient farming practices, which represents an inevitable trajectory towards the development of intelligent farming [1]. The current application of machine vision, deep learning, Internet of Things (IoT), artificial intelligence (AI), and other advanced technologies in pig farming robots has effectively freed up labor by facilitating cleaning, inspection and feeding, as well as epidemic prevention and control [2; 3; 4; 5]. Improving production efficiency while reducing the risk of interspecies disease transmission has been demonstrated to have significant practical value and promising development prospects [6]. Navigation and positioning are the enabling technology for robots, as satellite signals cannot cover indoor pig farms. Therefore, the development of high-precision indoor positioning technology is of great importance for breeding robots. The ultra-wideband (UWB) positioning system offers several advantages, including low system complexity, strong anti-jamming capabilities, high transmission rates, and low power consumption. It enables precise positioning and range accuracy in the decimeter or even centimeter range [7], making it suitable for the localization of breeding robots in pig farms. By deploying positioning base stations throughout the farm and equipping therobots with positioning tags, staff can accurately determine the robot's position using a host computer. The accuracy of the tag position does not only depend on the localization algorithm, but the base station placement scheme has a great influence on the localization accuracy [8; 9; 10]. Some studies propose an optimization method based on the base station placement to improve the localization accuracy [11; 12; 13]. Dilution of Precision (DOP) is a critical parameter for evaluating positioning accuracy. It quantifies the relative geometric relationship between range error and positioning accuracy under equivalent range error conditions at the same base station. A lower DOP value indicates higher positioning accuracy [14; 15]. Root-mean-square error (RMSE) measures the difference between observed and true values and is an indicator of positioning error. A smaller RMSE value indicates a smaller error and thus evaluates the position estimation accuracy of UWB indoor positioning tags [16]. Variance is used to quantify the degree of dispersion in positioning data. By studying the principles and methods of base station deployment, this study achieves precise tag positioning by optimizing the deployment scheme, improving accuracy and stability, and providing a guarantee for high-precision positioning of breeding robots in pig farms. The paper introduces accuracy factors and their relationship with positioning accuracy, and investigates the effect of base station geometry on positioning accuracy [14; 17]. A study was carried out using GDOP analysis to compare the results of mobile device positioning in different locations within a circular area with simulation results. The resolved positioning in the closed area away from the base stations, which agrees well with the simulation results, provides valuable insights for precise positioning design and testing [18]. [PERSON] discovered that even if the range error is small, the localization error in a confined area is large. Therefore, a localization algorithm combining Kalman filtering (KF) and DOP is proposed to improve the accuracy and stability in such environments [19]. [PERSON] proposed a novel positioning algorithm specifically tailored for indoor applications, which was evaluated using mean-square error (MSE) and dilution of precision (DOP) as criteria to optimize base station placement and improve positioning accuracy [20].