# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 6 Conclusions  
Classification of LoS/NLoS scenarios is a crucial issue for high-precision indoor UWB positioning. This article proposes a novel lightweight network, 1D-CLANet, which combines CNN, LSTM, and SE attention mechanisms. Using UWB CIR data as input, this approach leverages the deep feature extraction capability of 1D-CNN, the time series processing strength of LSTM, and the SE attention mechanism's focus on critical features, significantly enhancing NLoS classification accuracy. Application results on public datasets show that 1D-CLANet achieves a binary classification accuracy of up to 96.88%, with precision, recall, and F1 scores improving by at least 5.83% compared to traditional ML and DL methods. In experiments involving NLoS multi-class recognition of common indoor obstacles, the proposed model also demonstrates excellent performance, achieving a classification accuracy of 95.58% and improving precision and recall metrics by 4.87% over other advanced methods such as MLP and HQCNN. Thanks to the SE attention mechanism, the proposed method effectively differentiates between features that other methods find confusing, such as LoS and human features. Compared to methods without any attention mechanism like CNSM or those incorporating other attention mechanisms, 1D-CLANet achieves a balance of optimal performance and lower processing time (100.31 s). In future work, we will continue to improve the NLoS identification model proposed in this paper and explore its application in indoor positioning systems by integrating it with ranging and localization error mitigation methods.  
Conceptualization, Q.W.; methodology, Q.W. and C.Z.; software, Q.W. and J.L.; validation, X.Y.; formal analysis, Q.W. and J.L.; investigation, J.L.; resources, X.Y. and K.L.; data curation, Q.W.; writing--original draft preparation, Q.W., J.L. and X.Y.; writing--review and editing, M.C. and Y.L.; visualization, Q.W. and J.L.; supervision, M.C.; project administration, Y.L.; funding acquisition, M.C. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China, National Natural Science Foundation of China under Grant 202 ZYFB3706902 and 52305563.  
The data presented in this study are available on request from the corresponding author.  
The authors express their gratitude to engineer [PERSON] from dalian Haoru technology for his contributions to code debugging and experimental work.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Abbreviation** & **Full Name of Abbreviation** \\ UWB & Ultra-Wideband \\ NLoS/LoS & Non-Line-of-Sight/Line-of-Sight \\
1D-CLANet & 1D-ConvLSTM-Attention network \\ CIR & Channel impulse response \\ CNN & Convolutional neural network \\ LSTM & Long Short-Term memory \\ SE & Squeeze-and-Excitation \\ IoT & Internet of things \\ IPS & Indoor positioning systems \\ TX & Transmitter \\ RX & Receiver \\ ToF & Time of flight \\ ML & Machine learning \\ DL & Deep learning \\ \end{tabular}