## 4 Experimental Results
### Performance of DOA Estimation Model

Table 1: RMSEs of DOA estimates, considering a fixed number of snapshots at 500 and SNR ranging from \(-20\) dB to \(20\) dB. Observing Figure 8b and Table 2, it is evident that as the number of snapshots increases, the RMSEs of all methods decline. However, MUSIC and ASMUSIC exhibit relatively large errors, whereas the other algorithms show improved performance. The proposed model extracts spatial, time domain information from the signal array, and the noise weight is reduced through the spectrum attention mechanism. At a snapshot count of 50, the RMSE of the proposed model is 0.38\({}^{\circ}\). When the snapshot count reaches 500, the RMSE decreases to 0.01\({}^{\circ}\), thus achieving better results. This experiment shows that the model proposed by us retains its advantage even at low snapshot numbers, indicating that the filtering effectiveness of the SAM module remains unimpaired by a limited number of snapshots. **Experiment 3:** Assessment of prediction accuracy as SNR increases. The simulated conditions for Experiment 3 mirror those of Experiment 1. Figure 9a and Table 3 depict the prediction accuracy of the proposed network alongside the compared methods as the SNR varies. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **50 Snapshots** & **80 Snapshots** & **100 Snapshots** & **200 Snapshots** & **500 Snapshots** \\ \hline MUSIC & 12.78 & 10.61 & 9.87 & 6.29 & 1.07 \\ ASMUSIC & 10.79 & 9.18 & 8.44 & 5.10 & 0.22 \\ DeepMUSIC & 10.65 & 9.63 & 8.08 & 5.02 & 0.15 \\ ILSSE & 9.92 & 8.12 & 7.77 & 4.75 & 0.14 \\ CNN & 0.51 & 0.37 & 0.34 & 0.20 & 0.09 \\ LSTM & 0.63 & 0.45 & 0.40 & 0.28 & 0.08 \\ CNN-LSTM & 0.44 & 0.35 & 0.30 & 0.15 & 0.02 \\
**CNN-LSTM+SAM** & **0.38** & **0.32** & **0.23** & **0.08** & **0.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSEs of DOA estimates, considering a fixed SNR of 5 dB and with snapshots ranging from 50 to 500. Figure 8: RMSEs of DOA estimates. (**a**) Snapshots \(T=500\), \(\text{SNR}\in[-20,20]\) dB. (**b**) SNR = 5 dB, snapshots \(T\in[50,500]\). The data presented in Figure 9a and Table 3 reveal that the prediction accuracy of the MUSIC, ASMUSIC, DeepMUSIC, ILSSE, CNN, LSTM, CNN-LSTM hybrid models, and our proposed model all improve as SNR increases. Among these, our proposed model exhibits the highest level of performance, achieving an accuracy rate of 85% at \(-5\) dB SNR and reaching a perfect accuracy of 100% at 5 dB SNR. **Experiment 4:** Evaluation of prediction accuracy as snapshots increase. The experimental setup for Experiment 4 mirrors that of Experiment 2, while the criteria for assessing DOA prediction accuracy align with those used in Experiment 3. Figure 9b and Table 4 present a comparative analysis of prediction accuracy between the proposed networks and other methods, highlighting the impact of varying the number of snapshots. Both the graphical illustration in Figure 9b and the numerical results in Table 4 reveal that with an increasing number of snapshots, the accuracy rates of all methods undergo different levels of enhancement. Notably, the proposed network surpasses its competitors, achieving a full accuracy rate of 100% at 500 snapshot counts, marking an improvement of roughly 3% in comparison to other algorithms. This superior performance can be attributed to the trainable mask, which functions akin to a filter, effectively suppressing noise and enhancing the performance.