# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Satellite Information Data Mining  
Satellite azimuth and altitude angles indicate the position of a satellite in space. The azimuth angle is measured relative to true north, ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\), while the altitude angle ranges from 0\({}^{\circ}\) to 90\({}^{\circ}\). Based on the distribution of visible satellite azimuth and altitude angles, it is possible to roughly infer areas of obstruction at the current location. Figure 2 illustrates this, showing the satellite zenith maps at various locations. At the indoor west-side window, 90% of the visible satellites are distributed between 150\({}^{\circ}\) and 345\({}^{\circ}\). Similarly, at the indoor south window, over 90% of visible satellites are located between 75\({}^{\circ}\) and 210\({}^{\circ}\). In deeper indoor environments, only four visible satellites are detected, while in outdoor open areas, the satellites are more uniformly distributed. The altitude angle can also serve as a feature for scene classification. Based on the above analysis, we divide the azimuth angle into 24 regions, each spanning 15 degrees, and construct a 24-dimensional feature vector to represent the satellite distribution. For each region, a value of 0 indicates no visible satellites, while a value of 1 indicates the presence of visible satellites. Additionally, the percentage of regions containing visible satellites relative to the total number of regions is also used as a feature input, which is represented by the following equation:  
\[\text{P\_vis\_sate}=\frac{\sum_{i=1}^{24}\text{m}_{i}}{24} \tag{1}\]  
For precision analysis, we use the geometric dilution of precision (DOP) to assess how the spatial geometric distribution of observation satellites affects positioning accuracy. DOP is an indicator of position quality--higher DOP values indicate poor satellite geometry and  
Figure 2: Satellite zenith view: (**a**) west indoor neighboring window, (**b**) south indoor neighbouring window, (**c**) indoor, and (**d**) open outdoor neighboring window. lower precision, while smaller DOP values suggest a better satellite distribution and higher potential for precision. The Position Dilution of Precision (PDOP) represents the three-dimensional position accuracy, the Horizontal Dilution of Precision (HDOP) measures the accuracy in the horizontal plane, and the Vertical Dilution of Precision (VDOP) focuses on vertical accuracy [21]. These factors describe the positioning accuracy in their respective dimensions, and their relationship is expressed as follows:  
\[\text{GDOP}^{2}=\text{HDOP}^{2}+\text{VDOP}^{2} \tag{2}\]  
Researchers analyzed PDOP, HDOP, and VDOP statistics across various scenarios and derived their probability distributions, as shown in Figure 3. In Figure 3a, the graph represents the changes in accuracy factors from an open outdoor environment to a semi-outdoor environment. In open outdoor settings, the accuracy factors fluctuate less, but as the environment transitions to semi-outdoor, the fluctuations become more pronounced with 1127 calendar elements marking the transition. Figure 3b shows the changes in accuracy factors as the environment shifts from semi-outdoor to semi-indoor and then to a deep indoor environment. At 352 calendar elements, the environment transitions to an indoor state, where the terminal remains in tracking mode without immediate changes in accuracy factors. However, sudden large fluctuations occur indoors, which is likely due to weak satellite signals in certain indoor areas. Comparing the data from the open outdoor environment (calendar elements 0-1127) with the semi-outdoor environment (calendar elements 1128-3000), as shown in Tables 1-3, we analyzed the PDOP, HDOP, and VDOP values. It was observed that the minimum values in both outdoor and semi-outdoor environments were approximately the same, indicating that the minimum accuracy factor does not significantly differentiate these scenarios.