# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis
### Results and Analysis  
This paper evaluates the proposed 1D-CLANet method from three perspectives. First, we assess its effectiveness in traditional binary classification of LoS/NLoS scenarios. Second, we evaluate its effectiveness in classifying LoS and multiple NLoS scenarios to further validate the method. Lastly, we conduct ablation experiments to test the impact of integrating different attention mechanisms on the model's multi-class classification performance and training costs, confirming the effectiveness of incorporating the SE attention mechanism into our model. (1) Performance Evaluation of Traditional Binary Classification: To assess the performance of the 1D-CLANet model in traditional binary classification, we compared it with four ML (including DL) methods: CNN-LSTM (CNSM), ResNet (without ECA) [34], CNN [30], and SVM [26], and a traditional method based purely on signal processing: kurtosis identification [20]. Apart from CNSM and ResNet, which use the network structures shown in Figure 9a,b (with FC2 in Figure 9a and FC in Figure 9b set to 1), the other comparison methods, CNN [30] and SVM [26], employ the original model structures and parameters from [30] and [26]. The kurtosis identification method [20] determines an appropriate threshold by calculating the distribution of kurtosis of the NLoS and LoS CIR data from the training dataset. When the kurtosis of the input CIR is greater than or equal to this threshold, it is classified as LoS; otherwise, it is classified as NLoS. We adjusted the configuration of the training and test sets. The training data were selected from the first five scenarios of dataset [42]: office 1, office 2, a small apartment, a small workshop, and a kitchen with a living room. This amounted to a total of 30,000 sets of CIR data, with an equal split between LoS and NLoS samples. The performance evaluation was conducted using the remaining two scenarios of dataset: a bedroom and a boiler room, totaling 12,000 CIR datasets, also with an equal split between LoS and NLoS. This setup ensured a clear distinction between the training and testing data to validate the model's generalization ability. Except for SVM, which used manually extracted CIR features such as energy \(\varepsilon_{r}\), maximum amplitude \(r_{max}\), rise time \(t_{rise}\), mean excess delay \(\tau_{mel}\), Root-mean-square delay spread \(\tau_{rms}\), kurtosis \(k\), and estimated distance \(d_{t}\) as training inputs, all other comparison methods used normalized CIR signal data as the input, as shown in Table 6. The computation methods for the manually extracted CIR features can be found in [26]. To ensure a fair comparison, the hyperparameters and training configurations for ResNet and SVM were set according to [34] and [26], respectively. Since [30] did not provide training parameter configurations for CNN, the hyperparameters and training configurations from Table 4 and Section 4.4 were used for 1D-CLANet, CNSM, and CNN. The threshold value of 100.34 for kurtosis identification was determined using the method mentioned earlier. Table 7 presents the performance comparison of various methods for LoS/NLoS binary classification. It lists four evaluation metrics used to assess each ML approach. As shown, even in NLoS binary classification across different environments (where the training and \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Feature or Data Used** \\ \hline Kurtosis [20] & CIR feature \(k\) \\ SVM [26] & CIR feature \(e_{r}\), \(r_{max}\), \(t_{rise}\), \(\tau_{mel}\), \(r_{rms}\), \(k\) and \(d_{t}\) \\ CNN [30] & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\ ResNet (without ECA) [34] & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\ CNSM & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\
1D-CLANet & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data and features employed by different comparison methods for binary classification.