# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 6 Simulation Results
Figure 4: RMSE of DOA estimation performance for varying SNR when \(K=1\) and \(M=10\) at UCA receiver. Figure 5: RMSE of DOA estimation performance for varying SNR when \(K=2\) and \(M=10\) at UCA receiver. Figure 6 shows the RMSE performance versus the number of time-domain samples in a single signal-source environment for various SNR values. Basically, it is observed that the CI-based DOA estimation methods outperform the subspace-based MUSIC algorithm at low-SNR regions. Likewise, in the case of a single signal source, the performances of the CI and SCI techniques are almost the same to each other since both methods exploit sufficient time-domain samples to reduce the effect of noise. Figure 7 shows the RMSE performance for multiple signal sources in the same environment in Figure 6. As the number of samples increases, the multi-sample diversity improves the direction-finding performance, but the effect of the multi-sample diversity becomes saturated as the number of samples increases. It is worth noting that the proposed simultaneous CI technique significantly outperforms the MUSIC method regardless of the number of time-domain samples and operating SNR values. Figure 8 shows the RMSE performance for varying SNR values when the number of UCA antenna elements is equal to 5 (\(M=5\)) and two signal sources exist (\(K=2\)). As the number of antenna elements in UCA decreases, the corresponding beam width becomes wider. Then, the DOA estimation performance becomes gradually deteriorated in both MUSIC- and CI-based DOA estimation techniques. Nonetheless, the conventional and proposed CI-based techniques perform better than the MUSIC-based DOA estimation technique and result in more accurate DOA information when SNR is sufficiently high, even in the case of a small number of time samples (\(T=10\)). Furthermore, the proposed simultaneous CI technique achieves the best DOA estimation performance among all the considered DOA estimation techniques. When a sufficient number of time samples are obtained (\(T=100\)), the proposed simultaneous CI technique achieves a very elaborate DOA estimation performance even in a low-SNR regime. Figure 6: RMSE of DOA estimation performance for varying number of samples when \(K=1\) and \(M=10\) at UCA receiver. Figure 9 shows the RMSE performance of DOA estimation for varying SNR when the number of UCA antenna elements is equal to 20 (\(M=20\)) and two signal sources exist (\(K=2\)). In this figure, we assume a sufficient number of antenna elements for better DOA resolution with narrow beam width. When \(T=10\), the MUSIC-based DOA estimation technique results in the good RMSE performance only in a high-SNR regime, while the proposed simultaneous CI-based DOA estimation technique results in a good RMSE performance even in a low-SNR regime. On the other hand, when \(T=100\), the MUSIC-based DOA estimation algorithm performs better than the conventional CI-based DOA estimation technique in a high-SNR regime. It is worth noting that the proposed simultaneous CI-based DOA estimation technique is able to achieve the best performance. Figure 8: RMSE of DOA estimation performance for varying SNR when \(K=2\) and \(M=5\) at UCA receiver. Figure 7: RMSE of DOA estimation performance for varying number of samples when \(K=2\) and \(M=10\) at UCA receiver. -based DOA estimation technique results in the best performance regardless of the number of time samples and SNR values. Figure 10 shows the RMSE of DOA estimation performance for varying SNR when two signal sources exist in a similar direction. In this simulation, two signal sources are located in directions of \(\phi=\{40^{\circ},130^{\circ}\}\) and \(\theta=\{10^{\circ},5^{\circ}\}\), respectively. As mentioned earlier, the DOA estimation performance of the MUSIC-based algorithm becomes significantly deteriorated in this situation since the spatial correlation between two received signals increases.