# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 3 Results
### Experimental Comparison  
Several advanced algorithms have been selected for comparison. The first algorithm is based on the number of visible satellites. This method uses a fixed threshold for the number of visible satellites to make judgments. While it is simple, it has a high error rate and relies on a single source of information. The second algorithm also considers the number of visible satellites along with the carrier-to-noise ratio (CNR) and other information, classifying the data using machine learning. This approach utilizes various signal features from a single source and incorporates machine learning for more flexible judgments. However, it can be easily affected by the sensor state, leading to misjudgments. The third algorithm employs stacked integrated learning, which enhances scene classification ability, but its feature mining capabilities are insufficient, resulting in poor generalization. The fourth method combines convolutional neural networks (CNN) with long short-term memory (LSTM) networks for scene classification. The fifth method is the algorithm proposed in this paper. Both datasets were run ten times to calculate average values, and the results for the fixed threshold method are shown in Figure 16.  
The first method uses a fixed threshold approach, achieving a scene recognition accuracy of 90.85%. Although its threshold setting is straightforward, the scene-switching thresholds vary between cities, resulting in lower scene classification accuracy. The second method utilizes the number of visible satellites, carrier-to-noise ratio (CNR), and other information, classifying the data with machine learning to achieve an accuracy of 94.21%. This improvement over the first method is due to both the increased dimensionality of satellite data features and the enhanced accuracy of the machine learning model. However, it still relies on a single sensor signal, leading to weaker anti-interference capability. The third method employs stacked integrated learning, increasing scene classification accuracy to 96.29%. This method not only extracts features from a single satellite signal \begin{table}
\begin{tabular}{c c c} \hline \hline  & **CNN1 + CNN2 + BiLSTM** & **CNN1 + CNN2 + BiLSTM + WOA** \\ \hline Accuracy (\%) & 98.25 & 98.83 \\ Macro-F1 & 0.9810 & 0.9880 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of WOA experiment. Figure 16: Comparison of the accuracy of different models. but also leverages data from multiple sensors, resulting in richer features. However, it falls short in mining signal features adequately, leading to weaker generalization ability. The fourth method achieves an accuracy of 96.91% by using a CNN + LSTM network, which can extract scene features more effectively, yet it still struggles with correlating signal features and scene features. The fifth method combines a dual-scale CNN network with a BiLSTM network, reaching an accuracy of 98.25%. This approach excels at extracting features both locally and globally as well as from a sequential perspective, incorporating temporal, spatial, and mathematical statistical features from multiple sensors. It closely associates these features with scene labels, surpassing the accuracy of the previous four methods. Finally, the sixth method applies parameter optimization using the whale optimization algorithm (WOA) to the fifth method, achieving an accuracy of 98.83%, which is the highest among all the methods evaluated. Figure 17 presents a histogram showing the accuracy of the six methods across four scenarios, clearly illustrating how these algorithms classify different situations. The first method, based on fixed thresholds, achieves accuracy rates of 95.3% and 94.3% for indoor and outdoor scenarios, respectively. However, its performance drops significantly for semi-indoor and semi-outdoor scenarios with accuracies of only 87.1% and 86.7%.