## 7 Discussion
As shown in Table 6, adding Gaussian noise to the original BDS signal at 0 dB, 1 dB, 5 dB, 10 dB, 20 dB, 30 dB, and 40 dB corresponds to classification accuracies of 86.51%, 87.37%, 88.96%, 89.45%, 88.75%, 88.21%, and 87.41%, respectively. From the results, it can be seen that the CDAENet method performs better under 10 dB noise, and the classification accuracy can reach 89.45%. This is because the CDAENet method can effectively learn the features containing noisy data during the training process and improve its anti-interference ability. However, the performance is poorer when the BDS signal is almost completely flooded by noise, i.e., 20 dB, 30 dB, and 40 dB noise. Therefore, it can be concluded that filling in the appropriate Gaussian noise can effectively improve the classification performance of the model. 4. The normalization performances of different datasets are analyzed in Figure 15. The proposed normalization can always obtain the best classification performances in different noise conditions and different environments. In detail, normalization improves over no normalization by at least 0.78% and at most ty 1.72% on the four datasets, indicating that normalization is particularly important for our proposed model. Gaussian noise to the autoencoder to reduce the sensitivity of the model to the input data. Furthermore, to improve the detection performance of the model, we used the MLP method to detect the compressed nonlinear data. Therefore, we constructed the DAE-based DL model CDAENet to improve the classification performance of NLOS signals in urban forest environments. We collected noise-containing BDS signals from urban forest environments under four different sky-occlusion scenarios and validated the superiority of the CDAENet model through a variety of evaluation metrics. The experimental results show that the CDAENet-based method proposed in this article can converge quickly in all four urban forest environments. Our method essentially stops changing after 10 epochs. This result indicates that our model has a relatively fast convergence. Meanwhile, our proposed method has better classification results. Specifically, the classification accuracy is improved by 5.25% and 8.21% when compared to two well-known ML algorithms, SVM and DT, respectively. In particular, our proposed CDAENet method can achieve more than 95% classification accuracy and generally outperforms the benchmark methods in terms of F1-score, precision, and recall. In addition, the classification accuracy is improved by 8% and 3% over DL-based and ML-based methods, respectively. Notably, it solves the complex problem of classifying noise-laden BDS signals in practical engineering. Regarding the addition of different noise components, we tried the types of salt and pepper noise, uniform noise, and Gaussian noise. The statistical performance shows that Gaussian noise is the most effective. Filling in the appropriate noise can effectively improve the classification accuracy of the model, and adding 10 dB of noise improves the classification accuracy by about 2.94% and 2.04% compared with no noise and adding 40 dB of noise, respectively. The results show that the method effectively reduces the pollution of environmental noise during the training process and can further improve the classification performance under various sky-obscured environments in urban forests. In addition, urban forests have less signal masking, and environmental noise is the main reason affecting signal recognition. DAE can effectively mitigate environmental noise interference. Comparatively, mountainous areas or urban canyons are highly obstructed, and noise is not the most important influencing factor. Therefore, DAE can also be used in mountainous areas and urban canyons, but there may be more suitable methods to solve the NLOS recognition problem in these cases. Meanwhile, the normalization method can achieve higher classification accuracy than without normalization on the four datasets, and this result proves the importance of normalization.