## 4 Experimental Results
### Performance of DOA Estimation Model
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & \(\mathbf{-20}\) **dB** & \(\mathbf{-15}\) **dB** & \(\mathbf{-10}\) **dB** & \(\mathbf{-5}\) **dB** & **0 dB** & **5 dB** & **10 dB** & **15 dB** & **20 dB** \\ \hline MUSIC & 0\% & 0\% & 0\% & 0\% & 1\% & 95\% & 99\% & 100\% & 100\% \\ ASMUSIC & 0\% & 0\% & 1\% & 1\% & 15\% & 96\% & 100\% & 100\% & 100\% \\ DeepMUSIC & 0\% & 0\% & 1\% & 5\% & 20\% & 96\% & 100\% & 100\% & 100\% \\ ILSSE & 0\% & 0\% & 4\% & 10\% & 32\% & 96\% & 100\% & 100\% & 100\% \\ CNN & 0\% & 7\% & 15\% & 50\% & 70\% & 97\% & 100\% & 100\% & 100\% \\ LSTM & 0\% & 6\% & 15\% & 53\% & 75\% & 97\% & 100\% & 100\% & 100\% \\ CNN-LSTM & 1\% & 14\% & 25\% & 69\% & 88\% & 98\% & 100\% & 100\% & 100\% \\
**CNN-LSTM+SAM** & **6\%** & **25\%** & **40\%** & **85\%** & **99\%** & **100\%** & **100\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction accuracy of DOA estimates with a fixed number of snapshots at 500 and SNR ranging from \(-20\) dB to 20 dB. Figure 9: Prediction accuracy of DOA estimates. (**a**) Snapshots \(T=500\), SNR \(\in[-20,\,20]\) dB. (**b**) SNR = 5 dB, snapshots \(T\in[50,\,500]\). **Experiment 5:** Performance of DOA estimation under random angle. In the scenario of DOA estimation involving randomly chosen angles, the angular separation between the two sources is fixed at \(\Delta\theta=1^{\circ}\), and SNR is maintained at 0 dB. The DOA of the first source varies from \(-90^{\circ}\) to \(89^{\circ}\), with each step increasing by \(1^{\circ}\). The DOA of the second source ranges from \(-89^{\circ}\) to \(90^{\circ}\) with a step increment of \(1^{\circ}\). All the samples used for this analysis belong to the test set, aimed at validating the efficacy of the proposed method. The number of snapshots is fixed at \(T=500\). Experiment 5 demonstrates the performance of MUSIC, ASMUSIC, DeepMUSIC, ILSSE, CNN, LSTM, CNN-LSTM, and CNN-LSTM+SAM in terms of DOA estimation. The solid line indicates the actual DOA, while the estimated DOA is illustrated by the colored blocks. Figure 10a-h exhibit the DOA estimation abilities of the proposed networks in contrast to other methods. Upon examining Figure 10, it is evident that MUSIC, ASMUSIC, DeepMUSIC, ILSSE, CNN, LSTM, and CNN-LSTM exhibit significant DOA estimation errors. Conversely, the proposed model demonstrates the lowest estimation errors, suggesting superior DOA performance. **Experiment 6:** Performance of DOA estimation under specific angle. In this experiment, 100 samples are taken from the test set. The first DOA source of each sample is \(0^{\circ}\), and the second DOA source is \(1^{\circ}\). The SNR is set to \(-5\) dB and the number of snapshots is 200, and CNN, LSTM, DeepMUSIC, ILSSE, CNN-LSTM, and our proposed model are selected to predict these 100 samples; the prediction results are shown in Figure 11. From Figure 11a,b, it can be seen that except for the proposed model, the predictions of other algorithms show varying degrees of significant errors, indicating the accurate prediction ability of our model at low SNR. The success of our model can be attributed to several factors. Firstly, it leverages the strengths of both CNN and LSTM architectures, combining convolutional layers for feature extraction and recurrent layers for temporal dependency modeling. This hybrid approach allows the model to capture both the spatial and temporal characteristics of the signals, enhancing its ability to distinguish between closely spaced DOA sources. Secondly, our model uses an effective filter for colored noise. This filter operates as a preprocessing step, cleaning the input data before they are fed into the neural network. By reducing the noise level, it helps to improve the SNR effectively, even though the initial SNR is set to \(-5\) dB. This preprocessing step, combined with the advanced features and techniques of our proposed model, enables it to outperform other algorithms under low-SNR conditions.