## 7 Discussion  
In the experimental part, we validate the performance of our proposed CDAENet method under various sky-occlusion scenarios in an urban forest environment. The results show that our proposed CDAENet method not only converges quickly but also has good performance in all evaluation metrics. Compared with ML methods such as SVM and DT, we propose a more effective convolution-based DL method. In addition, unlike simple CNN and CSAE methods, the DAE approach effectively reduces the pollution of BDS data by environmental noise. Through the above analyses, we demonstrate the superiority of DAE in the recognition of NLOS in urban forest environments. DAE can effectively reduce the data dimension and weaken the noise of the data, so we can try to apply it to analyze the current hotspots of high-resolution remote sensing modern change detection [44] and the spatial-temporal distribution study of farmland [45]. In this article, we have analyzed and compared the proposed CDAENet method from various perspectives, such as convergence, classification performance, anti-interference ability, and normalization performance. Specifically, the detailed comparison between our proposed method and the existing methods is as follows. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Noise** & 0 dB & 1 dB & 5 dB & 10 dB & 20 dB & 30 dB & 40 dB \\ \hline
**Accuracy** & 86.51\% & 87.37\% & 88.96\% & **89.45\%** & 88.75\% & 88.21\% & 87.41\% \\ \hline \hline \end{tabular} The number in bold denotes it is the best result.  
\end{table} 
Table 6: Model under different noise components. Figure 15: Normalization performance evaluation of D1, D2, D3 and D4. 1. To compare the convergence performance of the proposed CDAENet method in detail, we provide the curves of epoch and loss under the MSE loss function for the training and validation sets. As shown in Figure 13, the convergence epochs of the proposed CDAENet method are basically the same on datasets D1, D2, D3, and D4 in the urban forest environment. When the epoch rises from 0 to 6, the loss decreases rapidly. At the same time, when the epoch rises from 6 to 10, loss essentially stops decreasing. To further verify this trend, we trained 12 epochs, at which point loss remained essentially unchanged relative to 10. Therefore, we can infer that 10 is the best epoch. At this point, the model shows excellent performance on all four datasets. 2. To demonstrate the superior classification performance of the CDAENet method, we conducted comparison experiments with the ML methods SVM and DT on four datasets. The comparison results are shown in Tables 1-4. On the four datasets, the CDAENet method outperformed the baseline learning method on most of the ACC, F1, Prec, and Rec metrics. Among them, the best performance is on dataset D3, which outperformed all baseline methods with ACC, F1, Prec, and Rec of 95.45%, 74.33%, 86.56%, and 73.21%, respectively. Meanwhile, we also observed that existing DL methods, such as the CNN and CSAE methods, outperform ML methods such as SVM and DT. Therefore, DL methods are more feasible than ML methods in solving the NLOS recognition problem. It is worth noting that the individual F1-score and recall values are not high, which is mainly due to the fact that some NLOS samples are not detected. For urban forest environments where most of the samples are LOS, the CDAENet method proposed in this paper is still valid. Compared to other metrics, classification accuracy is more important for this study. As shown in Figure 14, our proposed model outperforms other methods in classification accuracy across all metrics. 3. We report the performances of the CDAENet method for various noises with the best corresponding parameters to present their robustness against noises. As shown in Table 5, when we added salt and pepper noise, uniform noise, and Gaussian noise to the model, the classification accuracies on dataset D1 were 88.51%, 87.36%, and 89.45%, respectively. Therefore, we chose to add Gaussian noise to the model.