## 5 Deployment and Evaluation
### Pose Estimation for Vehicle Tracking  
The error and update ratio for vehicle pose estimation is evaluated in this section. Four tags were placed on the body of the vehicle to track its pose. Figure 6 displays the tag placement for the vehicle used in the experiment.  
Figure 7 shows the trajectory of the truck moving from the right of the tracking zone to the left. To measure the impact of blocking objects, we placed a bulldozer in front of anchor 4.  
Figure 6: Tag placement for vehicle pose estimation.  
The first group of our analysis focused on the LOS scenario. Table 3 shows the average estimated error ratio, average error, and update ratio of the three proposed methods, and Figure 8 illustrates these values for each repetition.  
When estimating the pose in scenarios without blocking objects, the LPF error correction method and the optimization-based pose estimation method introduced in ViPER reduced the error rate compared to the baseline method by 15% while providing a close update ratio. ViPER+ was also able to reduce the error ratio and average error compared to the baseline method and have the lowest average error among all other methods.  
We summarize the results of the scenario with blocking object (NLOS) in Table 4 and Figure 9. According to the results, NLOS signals have different impacts in each pose estimation method. In the baseline method, the number of estimation errors increased. In ViPER, in addition to higher error rates, the update ratio dropped by nearly 10% in the NLOS scenario. The presence of the bulldozer prevented the anchor from capturing all  
Figure 8: Results of vehicle tracking for scenarios without blocking objects. Using ViPER reduces the error rate in the pose estimation of the vehicle compared to the other two methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pose Estimation** & **Estimated Error** & **Average Error (m)** & **Update Ratio (\%)** \\
**Method** & **Ratio (\%)** & **Average Error (m)** & **Update Ratio (\%)** \\ \hline Baseline & 24 & 3.4 & 99 \\ ViPER & 9 & 2.6 & 95 \\ ViPER+ & 19 & 1.6 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pose estimation result for LOS scenario. ViPER+ provided the same update rate as the baseline method while reducing the error rate and average error compared to the baseline.  
Figure 7: Vehicle tracking for scenarios (**a**) without blocking objects and (**b**) with a blocking object.  
the signals from the tags. This increased the number of incomplete inputs which caused problems when estimating the pose with ViPER. According to Figure 9c, in the NLOS scenario, ViPER had a lower update ratio compared to the baseline.  
Since ViPER+ solved the problem of incomplete inputs in ViPER, we observed that this method provided the highest update ratio and lowest error ratio, and average error compared to previous methods. In some cases, such as repetition #3 in the LOS scenario, ViPER+ had an equal or higher error ratio compared to the baseline. Figure 10 depicts the output for repetition #3 of the LOS scenario using all the pose estimation methods. We marked the incorrect estimates with a different color to distinguish them from the correct estimates.  
According to Figure 10b, ViPER had the lowest pose estimation error rate in estimating the pose of the vehicle. In Figure 10c, although the number of incorrect estimates is high, estimated poses are closer to the ground truth pose compared to the incorrect estimates estimated with the baseline method in Figure 10a. Depending on the input, the ViPER+ input correction may not reduce the error to less than a meter. However, reducing the average error is beneficial for applications that can tolerate more error thresholds.