## 6 Experimental Results
### Comparison of Detection Performance  
#### 6.5.1 Numerical Results Comparison  
We have analyzed the performance of different detection models to demonstrate the effectiveness of this CDAENet model, as shown in Tables 1-4. The following conclusions can be obtained from the experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **Accuracy (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline SVM [13] & 85.20 & 46.94 & 70.13 & 50.41 \\ DT [14] & 81.24 & 75.19 & 74.77 & 75.64 \\ CNN [15] & 87.35 & 61.58 & 75.41 & 58.87 \\ CSAE & 88.81 & 67.96 & 79.48 & 63.48 \\ \hline
**CDAENet** & **89.45** & **77.44** & **82.94** & 70.36 \\ \hline \hline \end{tabular} The number in bold denotes it is the best result.  
\end{table}
Table 1: Performance comparison between CDAENet and other methods on the D1 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **Accuracy (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline SVM [13] & 84.89 & 47.06 & 44.44 & 50.00 \\ DT [14] & 82.78 & 57.62 & 57.58 & 56.66 \\ CNN [15] & 86.01 & 50.48 & 74.40 & 51.65 \\ CSAE & 86.87 & 53.10 & 77.64 & 52.99 \\ \hline
**CDAENet** & **88.73** & 55.53 & **78.06** & 54.09 \\ \hline \hline \end{tabular} The number in bold denotes it is the best result.  
\end{table}
Table 2: Performance comparison between CDAENet and other methods on the D2 dataset.  
Figure 13: MSE loss function value progress during model training. (**a**) D1, (**b**) D2, (**c**) D3, (**d**) D4.  
1. Overall, the CDAENet detection model shows superior detection performance in terms of ACC, F1, Prec, and Rec compared to almost all baselines. For example, on the D1 dataset, CDAENet achieves 89.45%, 77.44%, 82.94%, and 70.36% for accuracy, F1-score, precision, and recall, respectively.
2. It is worth noting that the value of recall is 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 algorithm proposed in this article is still effective. Meanwhile, although the DT model performs well in all performance metrics, its detection accuracy is not as good as that of the proposed model. This may be because CDAENet classifiers can benefit from the deep feature extraction of BDS, which improves the detection performance.
3. Compared with traditional ML models, CDAENet achieves very competitive and stable performance. Taking the dataset D1 as an example, CDAENet improves SVM and DT in detection accuracy by about 5.25% and 8.21%, respectively. The results show that the learned compression features of the BDS long-time series signals are effective. Speicifically, there is some information in the long-time BDS signals that is weakly helpful for the model to identify the signal type. On the contrary, the other part can be used as a key feature to identify its signal type, and our model effectively extracts this part of key features.
4. The proposed CDAENet method consistently outperforms the DL-based methods CNN and CSAE, which demonstrates the effectiveness of learning time series correlation using DAE. Compared to the normal CNN model, the CDAENet method can extract more information between time series.
5. The performance of DL-based methods is generally better than that of ML-based methods, which confirms that DL is more effective in learning data features.