# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 6 Conclusions  
In this paper, we propose a machine-learning-based path identification approach called PEiD, which utilizes time domain features extracted from the commodity WiFi devices to achieve real-time and accurate LOS/NLOS classification. Our method explores a novel feature called peak energy index distribution that can effectively reflect the randomness of the channel property. Compared with previous works, PEiD can significantly reduce the sample size required for identification while maintaining a high performance. A sliding-window-based algorithm is also adopted to solve the problem of mobility. No manual operations or inertial sensors are needed in the process.  
Nevertheless, our study also highlights two areas that require further improvement. Firstly, in indoor scenarios, the presence of rich multipath effects and environmental changes,  
Figure 12: The identification accuracy of LOS path with different time delay.  
such as different room layouts, can significantly impact channel properties. It is important to investigate whether the model trained in one environment can maintain its performance when confronted with a new scene. If the model's performance degrades substantially and necessitates retraining, the challenge of collecting laborious samples arises. Future research should address this issue to ensure robust performance across diverse environments. Secondly, the features selected in our study are still limited. While we utilized CSI and CIR, there are numerous unexplored combinations and possibilities. With the advancements in deep learning, future work can explore the extraction of more complex features using kernel methods or neural networks. This approach can potentially uncover hidden patterns and enhance the accuracy and robustness of wireless sensing systems.  
Overall, we believe that our work serves as a valuable supplement to existing methods for path identification. In the scenario of activity recognition, since our work can detect the blocking of direct paths, different data processing strategies can be made to eliminate discrepancies caused by channel state changes. In the scenario of user tracking that requires accurate localization at each time, our work can provide a real-time correction to the model when path status changes during the movement, ensuring precise trajectory estimation. PEiD achieves high path identification accuracy while maintaining a low identification delay, making it well-suited for practical applications.  
Conceptualization, Y.X., J.Z. and S.Y.; methodology, Y.X. and S.Y.; software, J.Z. and S.Y.; validation, Y.X. and J.Z.; formal analysis, Y.X. and J.Z.; investigation, J.Z. and S.Y.; resources, Y.X.; data curation, J.Z. and S.Y.; writing--original draft preparation, J.Z. and S.Y.; writing--review and editing, Y.X. and H.S.; visualization, J.Z. and S.Y.; supervision, Y.X., H.S. and S.Z.; project administration, H.S. and S.Z.; funding acquisition, Y.X. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China: 61902434; the Hunan Province Natural Science Foundation of China: 2023 JJ70016, 2023 JJ30702; The International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province (2021 CB1013); The Changsha Municipal Natural Science Foundation under Grant No. kq2208283.  
Not applicable.  
The authors declare no conflict of interest.