## 5 Conclusions  
This study provides insight into the measurement accuracy of UWB beacons based on TWR technology. The research methodology allowed for a detailed evaluation of the impact of diverse factors on measurement accuracy and the identification of areas for further improvement in this technology. A comparison of the results obtained using LIDAR and UWB beacons allowed for the assessment of the advantages and disadvantages of both technologies and indicated the areas in which each of these technologies can best be used. The methodology for testing the accuracy of UWB beacons based on TWR technology involved a series of experiments aimed at assessing the accuracy of measurements in various static and dynamic conditions. This study allowed us to gain a clearer insight into the effects of various factors on the measurement accuracy of UWB beacons and the identification of potential areas for improvement.  
Our research identified areas where the planned location system based on UWB beacons could be enhanced, for instance, when considering the variability of the phone's position or improving the location accuracy in NLoS conditions. To improve localization accuracy in NLoS conditions, the use of data fusion from different sensors can be considered. A significant insight may be revealed from integrating technologies to further enhance the level of geolocation services. As we live in a physical reality where interaction with spatial orientation-assisting devices occurs, the challenge lies in developing an algorithm that provides an effective collaboration between these technologies. Research aimed at enhancing localization accuracy for indoor navigation systems primarily focuses on integrating additional data from sensors such as accelerometers and magnetometers [90]. Other techniques include real-time filtering, dead reckoning algorithms with step detection, or point multiplication [91]. Emerging approaches in sensor fusion, combining UWB with vision, IMU, or other localization technologies, are also valuable for further research in this field [92]. Another emerging trend is the integration of UWB tracking with machine learning and AI. By harnessing the power of artificial intelligence algorithms, UWB tracking systems can intelligently analyze and interpret vast volumes of data generated by UWB sensors [93]. This enables the development of advanced tracking algorithms capable of adapting to changing environments.  
Numerous insights can be derived from the conducted research. First, in dynamic conditions, the accuracy of measurements obtained using LIDAR was higher than those obtained using UWB beacons. Secondly, to improve localization accuracy under NLoS conditions, the use of data fusion from various sensors can be considered. Finally, the development of UWB technology may lead to an increase in the availability of competitive hardware, enabling a more comprehensive assessment of the accuracy and effectiveness of this technology and the implementation of a well-functioning system.  
Conceptualization, M.R. and M.M.; methodology, M.R. and M.K.; software, M.R.; validation, M.M. and M.K.; formal analysis, M.R., M.K. and M.M.; investigation, M.R. and M.K.; resources, M.R. and M.K.; data curation, M.R.; writing--original draft preparation, M.R., M.K. and M.M.; writing--review and editing, M.R., M.K. and M.M.; visualization, M.R. and M.M.; supervision, M.R. and M.M.; project administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This publication was supported by the Excellence Initiative--Research University Program implemented at the Silesian University of Technology in 2024.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.