# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis
### Results and Analysis
Figure 9: Explanation of network structures: (**a**) CNSM, (**b**) ResNet without ECA. test sets are from different environments, and the test set includes two types of scenarios), the proposed method demonstrates higher generalization compared to other methods. Both 1D-CLANet and CNSM achieve over 91% on all four metrics, outperforming other traditional methods in recognizing LoS and NLoS scenarios. Compared to the baseline method CNSM, 1D-CLANet improves precision, recall, accuracy, and F1-score by 5.83%, 5.88%, 5.86%, and 5.85%, respectively. This indicates that incorporating the SE attention mechanism can enhance the model's binary classification accuracy. Among the five comparison methods, ResNet achieves the highest accuracy at 88.94%, followed by CNN at 82.97% and SVM at 79.66%. The kurtosis identification method, based purely on signal processing, performs the worst, with an accuracy of only 70.80%. This is because the selection of the CIR kurtosis threshold is limited by the environment. In a fixed environment where all obstacles have similar dielectric constants, a fixed-threshold kurtosis-based method can achieve relatively high accuracy. However, in this experiment, the training data came from five different environments, and the test data came from two other environments, leading to significant differences in dielectric constants between environments. This variation caused the kurtosis identification method to fail and resulted in low accuracy. Due to these shortcomings of pure signal processing methods, recent research tends to use ML or DL methods for UWB NLoS propagation identification to address the challenge of high generalization across different environments. For SVM, the lack of high-dimensional features from the CIR signal resulted in a poorer performance. Furthermore, Figure 10 presents the receiver operating characteristic (ROC) curves to further evaluate the effectiveness of the 1D-CLANet model. It is evident that the area under the curve (AUC) for both CNSM (0.969) and 1D-CLANet (0.996) is higher than that of other common approaches. This indicates that 1D-CLANet outperforms conventional methods in LoS/NLoS binary classification. In summary, integrating the SE attention mechanism with the LSTM output enhances the accuracy of LoS/NLoS binary classification. However, the applicability of the 1D-CLANet method to more complex scenarios remains to be further explored. (2) Performance Evaluation of NLoS Multi-Class Classification: To demonstrate the performance of our method in recognizing various NLoS scenarios, we compared it with LSTM, SVM [26], HQCNN [32], MLP [29], and the baseline method CNSM without any attention mechanisms. The LSTM model consists of 512 hidden units and two FC layers (LSTM-512-FC1-128-FC2-5, sequence). CNSM uses the network structure shown in Figure 8(a). Unlike in binary classification, the output unit of FC2 in the multi-classification task is set to 5. The other comparison methods, MLP [29], HQCNN [32], and SVM [26], use the original model structures and parameters from [29], [32] and [26], respectively. The training data for multi-class classification was collected from the stairway passage and office corridor scenarios shown in Figure 7(a),b, as detailed in Section 5.1. We used a total of 7000 CIR data samples from the office corridor scene depicted in Figure 7(b) for training, covering five types of CIR data: human II (1.68 m, 53 kg), glass IV (0.02 m), wooden door II (0.06 m), concrete wall II, and LoS II, with each category comprising 20% of the samples. For performance evaluation, we used a test set consisting of a total of 1857 CIR samples. This includes four categories of CIR data from the stairway passage scenario shown in Figure 7(a): human I (1.74 m, 64 kg, 497 samples), wooden door I (0.036 m, 325 samples), concrete wall I (245 samples), and LoS I (364 samples). Additionally, we incorporated CIR data for glass IV (0.02 m, 426 samples) from Figure 7(b).