## 4 Experimental Validation
### Empirical Tracking Algorithm
#### 4.3.2 Empirical Dynamic Positioning  
In the case of source rotation, although the direction of the source had an impact on the positioning result, there was no occlusion in the signal propagation. Therefore, at the point (2, \(-\)1.5), which was far away from the base station, the maximum error value was 0.595 m, and the positioning accuracy could also be guaranteed within 1 m. However, for dynamic scenes, factors such as signal occlusion and multipath cannot be avoided in the process of moving pedestrians equipped with information sources. Therefore, dynamic positioning experiments were carried out, and the dynamic experiment process was as follows.  
The user held the information source; the point (0, 2) was the starting point; the user traveled along the \(z\)-axis to the point (0, \(-\)2), then traveled along the positive direction of the \(x\)-axis to the point (2, \(-\)2), then turned to the positive direction of the \(z\)-axis to the point (2, 2), and finally, traveled along the negative direction of the \(x\)-axis to the starting point. The traveling diagram is presented in Figure 17, where the red circle indicates the starting position and green arrows indicate the direction of travel.  
The trajectory comparison diagram after filtering is presented in Figure 18, where the horizontal axis represents the x-coordinate, and the vertical axis represents the z-coordinate; the point position represented by the red circle is the original output value, the blue asterisk represents the output value of the STKF algorithm, the green plus sign indicates the output value of the SVD-STKF algorithm, and the rectangle formed by the solid black line represents the true trajectory. As shown in Figure 18, in the process of normal pedestrian walking, the signal occlusion was not obvious when the pedestrian was walking along the \(z\)-axis, and the positioning effect was good. However, when the pedestrian was far away from the base station, due to the decrease in the positioning accuracy and severe  
Figure 16: Distribution of rotation anchor points of the source. (**a**) (0, 0) location distribution. (**b**) (2, \(-\)1.5) location distribution.  
Figure 17: Reference trajectory of the dynamic experiment.  
signal occlusion, the positioning result had a large deviation. The offset of the farthest point from the real trajectory was close to 1 m, and the offset of the other anchor points was also large--almost all of them were above 0.5 m. The Hausdorff distance was 0.979 m. Therefore, in the dynamic process, due to the influences of signal occlusion and other factors, if the original output of the system is relied on, the positioning accuracy of distant points cannot be guaranteed. After the STKF correction, the trajectory was basically close to the real trajectory, the remote location points could be corrected, and the positioning results were smoother without random mutation points. The Hausdorff distance was increased to 0.519 m, achieving an increase of 0.46 m. On this basis, compared with the STKF algorithm, the SVD-STKF algorithm achieved more obvious corrections of the positioning results, making them be more close to the real trajectory; also, the Hausdorff distance was further increased to 0.464 m by 0.515 m.