# An Approach to Dynamic Sensing Data Fusion
## 3 Sensor Data Acquisition System
### Sensing Data Acquisition  
The acquisition of sensor data involves different devices, including the following:  
1. Location identification: data obtained from the sensor of the global positioning system (GPS) receiver
2. Number of satellites: data obtained from the sensor of the GPS receiver
3. Control pattern: data obtained from the ground control station
4. Type of reception: data obtained from the ground control station
5. Identification of reception: data obtained from the sensor of the RC receiver
6. Mark of automatic aerial photography: data obtained from the ground control station
7. Mark of cycling route: data obtained from the ground control station
8. Flight mode: data obtained from the sensor of the RC remote controller and the ground control station
9. Types of taking off and landing: data obtained from the sensor of the RC remote controller and ground control station
10. Longitude and latitude: data obtained from the sensor of the GPS receiver
11. Heading: data obtained from the sensor of the autopilot
12. Speed: data obtained from the sensor of the autopilot
13. GPS altitude: data obtained from the sensor of the GPS receiver
14. Baremetric height: data obtained from the sensor of the autopilot
15. Distance to the destination waypoint: data obtained from the sensor of the autopilot
16. Lateral deviation distance: data obtained from the sensor of the autopilot  
In order to facilitate research, the relevant variables are formalized, namely, location identification (x1), number of satellites (x2), control pattern (x3), type of reception (x4), identification of reception (x5), mark of automatic aerial photography (x6), mark of cycling route (x7), flight mode (x8), types of taking off and landing (x9), longitude (x10)and latitude (x11), heading speed (x12), GPS altitude (x13), barometric height (x14), distance to the destination waypoint (x15),and lateral deviation distance (x16):  
\[\text{X}=[\text{x1},\text{x2},\ldots,\text{x16}] \tag{2}\]  
Obviously, X is a 16-dimensional vector. In Figure 5, the current sampling rate is too large for data collection in the 101-125 s and 175-200 s time intervals. Therefore, the sampling rate needs to be reduced to capture the data changes within the two time intervals. In Figure 6, the sampling rate between 201 and 225 s should be maintained, and the sampling rate in the 226-300 s time interval should be reduced. The second phase of the experiment was environmental data collection. Here, the algorithm of dynamic regulation technique for the acquisition timeintervals was adopted, as shown in Figure 7.  
In Figure 7, according to the dynamic regulation technique, the collection effect in the 201-225 s time interval is basically the same as the changes in Figure 6.However, in the 226-300 s time interval, the collection effects has obvious enhancement, i.e., the collection effect is basically the same as the real changes and better than the results in Figure 6.  
In order to verify the effectiveness of this method, the data collected by the sensors were fused in stages. For the processing, the time estimation method [4], passive monitoring method [5], sampling method [6], and dynamic adaptive method were used, among which the first three are fixed acquisition intervals and the last one is dynamic acquisition interval. Table 1 shows a comparison between the sensor data dynamic collection algorithm and the algorithms of [4; 5; 6]. Figure 6: Distance to destination waypoint in the 201–300 s time interval. Figure 7: Result of dynamic regulation technique for the acquisition timeintervals. In Table 1, \"deviation to the desired value\" denotes the standard deviation between the measured values and the desired values, i.e., \(\text{Sqrt}(\left(\sum(\text{x}_{i}\text{ - x}_{i}\text{)}^{\prime}\text{2}\right)/\left(m-1\right)]\). In this formula, \(\sum\) denotes the summation, \(\text{x}_{i}\)' denotes the desired value of \(\text{x}_{i}\), \(m\) denotes the size of the sampling window, and Sqrt denotes solving the square root.