# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 5 Conclusions  
This study enhances previous AoA estimation methods in indoor localization systems by leveraging phase drift values and employing GRU-based neural networks. The key contributions of this work include:  
* Improvement in AoA estimation: the proposed PLAE model achieved a 99.95% improvement in mean absolute error (MAE) compared to traditional AoA methods, with a maximum deviation of only 6.77 degrees, demonstrating its robustness and accuracy in complex indoor environments.
* Effective handling of environmental challenges: the PLAE model is effective in addressing common challenges such as multipath effects, signal fluctuations, and environmental interferences often encountered in indoor environments.
* Novel approach with phase drift: this study introduces a novel approach by utilizing phase drift values for AoA estimation, confirming their effectiveness as an important feature for enhancing accuracy in complex environments.
* Real-time estimation capability: the PLAE model's low computational cost enables real-time estimation, making it highly suitable for IoT environments, smart buildings, and other indoor positioning systems.  
Figure 15: Comparative analysis of SGRU, GRU, and LightGRU layer models: (**a**) model loss comparison and (**b**) model inference time comparison.  
* Expansion of data collection: future research will focus on expanding data collection to include multiple environments, such as different room sizes, obstacles, and dynamic conditions, to further evaluate the model's generalizability.
* Applicability to outdoor environments: while this study is focused on indoor positioning, future work could explore its applicability to outdoor environments, where multipath effects and signal conditions vary significantly.
* Real-world deployment considerations: practical challenges related to sensor calibration, hardware constraints, and network integration will be essential for real-world deployment, especially for large-scale IoT applications and embedded systems.
* Optimized lightweight models: future work will also explore the development of optimized lightweight models that ensure efficiency without compromising accuracy for more complex indoor settings.
* Exploring probabilistic approaches: research will investigate probabilistic approaches such as Monte Carlo dropout and Bayesian GRU to enhance model interpretability and provide uncertainty estimation in AoA predictions.  
This study experimentally validates the potential of phase drift-based machine learning approaches for AoA estimation, contributing significantly to the improvement of indoor positioning system accuracy for future applications.  
Conceptualization, J.L.; methodology, S.K. and J.L.; software, S.K.; validation, S.K. and J.L.; formal analysis, S.K. and J.L.; investigation, S.K.; resources, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, J.L.; visualization, S.K. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (grant number: 2022 RIA2C1009951).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.