# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 5 Simulation Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Description** & **Value** \\ \hline \(P_{0}\) & Reference power & 20 (dBm) \\ \hline \(\gamma\) & Path loss exponent & 3 \\ \hline \(d_{0}\) & Reference distance & 1 (m) \\ \hline \(\text{bias}_{\text{max}}\) & Magnitude of NLOS bias & \(B/5\) (dB, m) \\ \hline \(\sigma_{i}\) & Noise power & 3 (dB, m) \\ \hline \(B_{t}\) & Area border for targets in the second scenario & 15 m \\ \hline \(|\mathcal{L}_{\text{n}}|\) & Number of NLOS links & \(N\) \\ \hline \(T_{\text{max}}\) & Max number of iteration for SR-WLS & 2 \\ \hline \(\epsilon_{\text{min}}\) & Lower limit for \(\epsilon\) & 0.25 \\ \hline \(\epsilon_{\text{max}}\) & Upper limit for \(\epsilon\) & 0.75 \\ \hline \(M_{c}\) & Number of Monte Carlo runs & 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the Fixed Simulation Parameters. Figure 2 illustrates the RMSE (m) versus \(N\) performance of R-GTRS estimator in [22] for different \(B\) in the two considered scenarios. The figure shows that the counterpart of the estimator employing RSS-only measurements is the best option for low \(B\) in general. However, when \(B\) is increased this counterpart worsens significantly, as expected, and the hybrid version of the estimator becomes the best option. Actually, one can see that measurement integration is the best choice when \(N\) is low for all \(B\). This is not surprising, since it benefits from the double information gathered by each anchor, whereas the _traditional_ counterparts do not have that luxury, and have a limited amount of information. When \(B\) is set to its highest considered value, it was expected that the TOA-only counterpart outperforms the other options; however, this is not quite the case, especially in the first considered scenario. This can be explained to some extent by the fact that, in the first scenario, random deployment of all sensors is considered and there were no guaranties that the actual inter-sensor distance was large. Nevertheless, this can not be said in the second scenario, where the hybrid estimator and its TOA-only counterpart exhibit practically the same performance. Figure 3 illustrates the RMSE (m) versus \(N\) performance of SR-WLS estimator in [21] for different \(B\) in the two considered scenarios. Principally, very similar conclusions to those of Figure 2 can be made from Figure 3: the best option for low \(B\) is the RSS-only counterpart in general, whereas the hybrid estimator dominates over the remaining ones as \(B\) is increased. Once again, the TOA-only  
Figure 2: Root mean squared error (RMSE) versus \(N\) comparison for R-GTRS. counterpart practically matches the performance of the hybrid estimator in the second scenario for the highest considered value of \(B\). Figure 4 illustrates the RMSE (m) versus \(N\) performance of HWLS estimator in [18] for different \(B\) in the two considered scenarios. For the first scenario, the figure corroborates earlier conclusions, but leaves the RSS-only counterpart as the worst option in the second scenario for all considered \(B\). This result was not anticipated, and no explanation for this behavior is provided here. Figure 5 illustrates the RMSE (m) versus \(N\) performance of NR and JAH estimators in [20; 23] respectively, for different \(B\) in the two considered scenarios. Note that the two estimators utilize both RSS and TOA measurements to obtain the range between the target and anchors, and that their decomposition into RSS-only and TOA-only counterparts is not straightforward. Therefore, only the hybrid version of the algorithms is considered here. Figure 5 exhibits that JAH is favorable in scenarios with low \(B\), whereas NR is a better option than JAH when \(B\) is increased. Figure 3: RMSE versus \(N\) comparison for SR-WLS. Moreover, Figure 6 illustrates the RMSE (m) versus \(N\) comparison of SR-WLS estimator in [21] in the first considered scenario when NLOS biases are randomly chosen from a uniform distribution.