# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis
### Results and Analysis
Despite this, the overall accuracy of the SE-enhanced 1D-CLANet model remains superior to the model without any attention mechanisms (CNSM). (3) The impact of different attention mechanisms: To validate the effectiveness of integrating the SE attention mechanism into our model's feature extractor, we conducted ablation experiments comparing the following: (1) no attention mechanism, which omits the feature fusion layer and outputs LSTM features directly to the FC layer, as shown in Figure 9a; (2) 1D-CLANet combined with self-attention, which numHeads = 4, numKey-Channels = 512; (3) 1D-CLANet combined with coordinate attention; and (4) our proposed 1D-CLANet combined with SE attention. Table 9 shows the feature extraction layer structures, and for fairness, CA and SE attention mechanisms are configured with numChannels = 512 and reductionRatio = 4, using the same datasets and hyperparameters from the previous multi-classification experiment as described in Table 4 and Section 4.4. Table 10 summarizes the multi-class recognition accuracy, training time, and number of trainable parameters for each model on the NLoS multi-classification test set, where training time and accuracy are averaged over 100 experiments. Our proposed 1D-CLANet with the SE attention mechanism achieves the highest overall recognition rate of 95.58% across the five scenarios, with a parameter count similar to the original model without any attention mechanism (1.4 M) and a shorter training time (100.31 s) compared to models with other attention mechanisms. The accuracy improves by 5.6% over the 1D-CLANet without attention. While the 1D-CLANet+self and 1D-CLANet+CA methods also show excellent performance, with multi-classification accuracies of 93.37% and 94.91%, respectively, our model outperforms them, indicating that the SE attention mechanism enhances recognition accuracy by more effectively focusing on important features. Although CA mechanisms are beneficial for emphasizing features in image recognition tasks, they may not perform as well as SE attention in extracting features from sequence signals like UWB CIR. Figure 11: Performance comparison of different methods for NLoS multi-classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extractor** & **Parameters/M** & **Accuracy/\%** & **Time/s** \\ \hline
1D-CLANet without & 1.2 & 89.82 & 85.43 \\
1D-CLANet with Self & 2.3 & 93.37 & 143.79 \\
1D-CLANet with CA & 1.5 & 94.91 & 120.82 \\
1D-CLANet with SE & 1.4 & 95.58 & 100.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of identification accuracy with various attention mechanisms. Figure 12: Confusion matrix outcomes for multiclassification. The Sec.1, Sec.2, Sec.3, Sec.4, and Sec.5 correspond to LoS, human, glass, door, and wall, respectively. **(a)** LSTM. **(b)** SVM. **(c)** HQCNN. **(d)** MLP. **(e)** CNSM. **(f)** 1D-CLANet. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **1D-CLANet** & **1D-CLANet** & **1D-CLANet** & **1D-CLANet** \\
**without Attention** & **with Self** & **with CA** & **with SE** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Differences in feature extractors of compared models.