## 4 Experimental Validation and Analysis  
### Experimental Validation of the Improved Algorithm Model  
The effectiveness of the proposed improved CKF algorithm compared with the traditional SRD algorithm model for solving UWB indoor localization position was experimentally verified and evaluated in an indoor environment in this study. In the experiment, the indoor environment was selected as an experimental space with a size of 9 m \(\times\) 6 m. The laboratory was equipped with tables, computers, and other obstacles that could be verified for non-line-of-sight distance. The experimental platform was the UWB indoor localization system developed by Zhengzhou Lianrui Electronic Science and Technology Co. (Zhengzhou, China). The system utilized the TDOA algorithm to obtain the UWB location tag result, enabling subsequent data processing. In the experimental process, the relevant personnel held UWB positioning tags along the table edge to carry out dynamic trajectory measurements. The coordinates of the four positioning base stations were represented as (0 cm, 0 cm, 189 cm), (0 cm, 720 cm, 189 cm), (400 cm, 0 cm, 189 cm), and (400 cm, 720 cm, 189 cm). When reading data from a UWB ranging sensor, the refresh  
Figure 2: Overall structure of the improved CKF algorithm. frequency of programmable logic controller (PLC), set at 50 Hz, is followed according to communication protocol requirements. The ranging accuracy of UWB is 50-100 cm. The data processing software was matlab2016b. The experimental scenario is shown in Figure 3.  
This study only considered the localization results on the two-dimensional plane since this experiment was carried out on a smooth indoor floor and the UWB positioning tag changes extremely little under elevation. In the case of no obstacle occlusion, the UWB positioning signal was not occluded by the personnel when they held the UWB positioning tag for trajectory measurement along the table edge. During the measurement process, some feature points (such as the table corner, etc.) were selected as the reference checkpoints of the whole localization process to evaluate the algorithmic accuracy of the UWB localization system. In the case of obstacle occlusion, such as the relevant personnel holding tightly onto the UWB positioning tags, non-line-of-sight conditions were realized. The rest of the experimental operations were the same  
Figure 5: Trajectory fitting of each algorithm for UWB indoor localization. Figure 6: X-axis error of reference checkpoint. Figure 7: Y-axis error of reference checkpoint. Figure 4 represents the dynamic trajectory before filtering is carried out by the SRD algorithm and after filtering is carried out by the SRD + CKF algorithm, and the real motion trajectory. The dynamic trajectory after the combined SRD + CKF algorithm is smoother and more concentrated than the trajectory before filtering. The whole algorithm has fewer points with larger deviations away from the real trajectory, which indicates that the CKF algorithm can improve the stability of the whole localization system. Figure 5 shows the dynamic trajectories of the SRD algorithm before filtering, the SRD + CKF algorithm, and the improved SRD + CKF algorithm trajectory. The dynamic trajectory points of the improved SRD + CKF algorithm are more intensive compared with the unimproved algorithm, and have fewer dispersion points that deviate from the trajectory, which is closer to the real trajectory. In the case of obstacles, the discrete points of the improved filtering algorithm can remain concentrated for some time. There are fewer dispersion points than in the unimproved algorithm, which indicates that the Sigmoid function can strengthen the system's overall stability based on the CKF algorithm. The advantages of the Sigmoid function are also better reflected. The parameters of the Sigmoid function in this study were obtained as \(\lambda=1,\mu=0.1\) by conducting several experiments to better verify the specific effect of the proposed algorithm.