# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 7 Conclusions  
In this paper, we proposed a novel simultaneous Correlation Interferometer (CI) technique for improving Direction of Arrival (DOA) estimation performance, which effectively exploits multiple time samples and can be considered a generalization of the conventional CI technique. We first investigated the operation procedure of an existing CI algorithm in detail and discussed its technical challenges. As a novel solution, we proposed a simultaneous CI technique that allows the multi-antenna receiver to jointly combine the spatial characteristics of each time sample for improving direction-finding performance  
Figure 12: RMSE of DOA estimation performance with ULA structure according to SNR when two signal sources exist (\(K=2\)).  
Figure 13: RMSE of DOA estimation performance with ULA structure according to SNR when three signal sources exist (\(K=3\)).  
thanks to the multi-sample diversity effect. Through computer simulations, we showed that the proposed simultaneous CI technique significantly outperforms the conventional DOA estimation techniques with various array structures, Signal-to-Noise Ratio (SNR) values, and the number of time samples. It is worth noting that the proposed simultaneous CI technique has a better DOA estimation performance than classical MUSIC algorithms in terms of DOA estimation performance, especially with a small number of time samples and antenna elements. As a result, the proposed simultaneous CI-based DOA estimation algorithm can be effectively applied to various dynamic DOA estimation systems, such as 5G/6G wireless communication systems, radar systems, multiple antenna-based DOA beamforming systems, GNSS anti-spoofing/anti-jamming systems, military communication systems, etc. As further work, we plan to implement the proposed technique to consider its hardware complexity and feasibility.  
Conceptualization, B.C.J. and I.-K.L.; methodology, B.C.J.; software, M.O.; validation, B.C.J. and I.-K.L.; formal analysis, B.C.J.; investigation, M.O. and Y.-S.L.; resources, B.C.J.; data curation, M.O. and Y.-S.L.; writing--original draft preparation, M.O. and Y.-S.L.; writing--review and editing, B.C.J. and I.-K.L.; visualization, M.O. and Y.-S.L.; supervision, B.C.J.; project administration, B.C.J. and I.-K.L.; funding acquisition, B.C.J. and I.-K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Intelligent Technology Development Program on Disaster Response and Emergency Management (2022-MOIS37-005).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this paper:  
\begin{tabular}{l l} IIoT & Industrial Internet of Things \\
3 GPP & 3 rd Generation Partnership Project \\ IEEE & Institute of Electrical and Electronics Engineers \\ DOA & Direction of Arrival \\ UAV & Unmanned Aerial Vehicle \\ V2X & Vehicle to Everything \\ GNSS & Global Navigation Satellite System \\ MLE & Maximum Likelihood Estimator \\ MUSIC & Multiple Signal Classification \\ IMLC & Iterative Maximum Likelihood Calibration \\ ESPRIT & Estimation of Signal Parameters via Rotational Invariance Techniques \\ CS & Compressed Sensing \\ CI & Correlative Interferometer \\ ULA & Uniform Linear Array \\ UCA & Uniform Circular Array \\ SNR & Signal-to-Noise Ratio \\ LOS & Line of Sight \\ AWGN & Additive White Gaussian Noise \\ LEO & Low Earth Orbit \\ MIMO & Multiple-Input Multiple-Output \\ GPS & Global Positioning System \\ RMSE & Root Mean Squared Error \\ \end{tabular}