# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Different SNR Environments  
To assess the robustness of the proposed TSNetIQ model under varying noise conditions, experiments were conducted across multiple SNR levels, with 1000 test samples generated for each level. These samples were fed into three trained models--TSNetIQ, ResNetIQ, and CNN--with each sharing identical training configurations in terms of array element count \(M\) and sampling frequency \(f_{s}\). The performance was quantitatively evaluated using RMSE, and further analyzed via error histograms, error scatter plots, and detailed tabulated prediction distributions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network**Complexity & **FLOPs** & **Params** \\ \hline CNN & \(5.78\times 10^{7}\) & \(6.78\times 10^{4}\) \\ TSNetIQ & \(2.66\times 10^{8}\) & \(6.05\times 10^{5}\) \\ ResNetIQ & \(1.90\times 10^{8}\) & \(2.46\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FLOPS and Params of different models. Figure 5: Performance of the loss curves of the three compared models. Table 3 presents a breakdown of the prediction error ranges for the three models across the 1000 test samples. TSNetIQ demonstrates an outstanding concentration of predictions within the 0\({}^{\circ}\)-5\({}^{\circ}\) error interval, accounting for 99.9% of all predictions (999/1000), while only a single sample fell into the 5\({}^{\circ}\)-10\({}^{\circ}\) category and none exceeded a 10\({}^{\circ}\) error. In contrast, ResNetIQ and CNN exhibit degraded precision, with 12 and 14 predictions, respectively, falling into the moderate error range, and even a small number of cases (3 and 9, respectively) exceeding 10\({}^{\circ}\) of error. This clear contrast reflects the superior noise-resilience and angle resolution of TSNetIQ under variable acoustic conditions. To substantiate this statistical observation, Table 4 illustrates ten representative prediction results from TSNetIQ. The predicted angles are shown to deviate from the ground truth by no more than 0.2\({}^{\circ}\), with most results showing absolute errors as low as 0.1\({}^{\circ}\) or even zero. Notably, the model maintains this high level of precision across the entire angular space (e.g., from 0\({}^{\circ}\) to 335\({}^{\circ}\)), underscoring its consistency and reliability in directional inference. Visual analysis through error histograms (Figure 6) further confirms these findings, the errors of TSNetIQ are sharply concentrated near zero, while ResNetIQ and CNN yield broader distributions, signifying more frequent moderate-to-large deviations. Likewise, scatter plots (Figure 7) comparing predicted versus true angles indicate that TSNetIQ predictions closely adhere to the ideal \(y=x\) line, while other models suffer from noticeable dispersion, particularly at angle extremities. As shown in Figure 8, it is observed that the average prediction error and RMSE for all three models decrease with increasing SNR, as expected. However, this improvement trend becomes markedly slower at high SNR levels. Interestingly, due to the model being trained specifically at 5 dB SNR, the CNN model paradoxically exhibits performance degradation when evaluated at higher SNRs--suggesting poor generalization. By contrast, the TSNetIQ model maintains stable accuracy across the entire SNR spectrum, demonstrating a stronger generalization capability and robustness to unseen noise conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**True Angle/Degree** & **Predicted Angle/Degree** & **Error** \\ \hline
0 & 0.1 & 0.1 \\
9.4 & 9.3 & 0.1 \\
68.8 & 68.8 & 0 \\
102.9 & 102.7 & 0.2 \\
103.3 & 103.2 & 0.1 \\
154.7 & 154.7 & 0 \\
233.3 & 233.2 & 0.1 \\
257.2 & 257.1 & 0.1 \\
315.2 & 315.2 & 0 \\
335.3 & 335.2 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Examples of DOA predictions using TSNetIQ.