# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis
### Results and Analysis
Compared to the training dataset, the glass IV CIR data in the test set has different anchor-to-tag distances to ensure a clear distinction between the training and testing data. This distinction allows for a fair evaluation of the model's multi-class classification performance. The MLP [29] method used both normalized CIR data and manually extracted features: first path strength difference (FPSD), first path distance difference (FPDD), number of pseudo peaks (NPP), \(\tau_{med}\), \(\tau_{rms}\), and \(k\). SVM [26] used manually extracted CIR features consistent with those in the binary classification experiment. The other comparison methods used normalized CIR signal data as input, as shown in Table 8. The calculation methods for the manually extracted CIR features used in SVM and MLP can be found in [26; 29], respectively. To ensure a fair comparison, the hyperparameters and training configurations for MLP, HQCNN, and SVM were based on [26; 29; 32], respectively. The hyperparameters and training configurations for 1D-CLANet, CNSM, and LSTM were based on those in Table 4 and Section 4.4. Figure 11 illustrates the precision, recall, F1, and accuracy of four common approaches, CNSM without any attention mechanisms and the 1D-CLANet method. The performance of common techniques declines while handling multi-class scene classification, particularly for SVM and LSTM-related methods, indicating that these approaches cannot capture enough features to differentiate between various scenes. Clearly, both CNSM and 1D-CLANet outperform traditional methods in multi-scene classification. Compared to CNSM, the 1D-CLANet shows improvements of 4.87% in precision, 6.32% in recall, 5.88% in accuracy, and 5.76% in F1. Unlike CNSM, the 1D-CLANet incorporates the SE attention mechanism, which aids in distinguishing scenes with similar CIR characteristics, thereby enhancing overall performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Feature or Data Used** \\ \hline LSTM & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\ SVM [26] & CIR feature \(\mathbf{\epsilon}_{r}\), \(\mathbf{r}_{max}\), \(\mathbf{r}_{rise}\), \(\mathbf{\tau}_{med}\), \(\mathbf{\tau}_{rms}\), \(k\) and \(d_{t}\) \\ HQCNN [32] & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) \\ MLP [29] & CIR signal \(\mathbf{Z}=[z^{1},z^{2},\dots,z^{L}]\) and/CIR feature FPSD, \\  & FPDD, NPP, \(\mathbf{\tau}_{med}\), \(\mathbf{\tau}_{ms}\) and \(k\) \\ 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 8: Data and features employed by different comparison methods for multi classification. The confusion matrix provides detailed information on the classification performance of various methods. As shown in Figure 12a-d, Sec. 1 to Sec. 5 correspond to LoS, human, glass, wooden door, and wall obstruction scenarios, respectively. CNSM, MLP, and HQCNN achieved a certain degree of multi-scene recognition. As depicted in Figure 12d,f, MLP even outperformed 1D-CLANet in terms of classification accuracy for Sec. 4. However, misclassifications in scenarios such as Sec. 1, Sec. 2 and Sec. 4 led to overall poor performance for MLP, HQCNN, and SVM. While LSTM performs well in time series prediction, it struggles to extract sufficient features from CIR signals for LoS/NLoS classification with minimal time complexity, particularly in distinguishing Sec. 5, where it performs poorly compared to other models. The proposed 1D-CLANet, even without the attention mechanism (CNSM), achieved significantly better accuracy (95.58%, 89.82%) compared to MLP, HQCNN, SVM, and LSTM methods (85.68%, 80.24%, 76.95%, and 74.04%, respectively), as shown in Figure 12e,f. With the SE attention mechanism, 1D-CLANet improved the recall rates for Sec. 1 and Sec. 2 to 98.9% and 89.54%, respectively, which other methods struggled to achieve. However, the recognition accuracy for Sec. 5 decreased due to SE prioritizing other features.