# An Approach to Dynamic Sensing Data Fusion
## 2 Dynamic Acquisition and Fusion
### Data Change Decision
The basic unit of dynamic adjustment technology based on acquisition time interval is the time slice: When \(\left|x_{i}-x_{i}^{\prime}\right|/\overline{D}_{i}^{m}>\varepsilon^{+}\), the collection timeinterval will decrease \(\Delta\tau\), and this judgment will be conducted periodically, i.e., if \(\left|x_{i}-x_{i}^{\prime}\right|/\overline{D}_{i}^{m}>\varepsilon^{+}\) still holds, then the acquisition timeinterval will continue to decrease \(\Delta\tau\) until the condition does not hold; when \(\left|x_{i}-x_{i}^{\prime}\right|/\overline{D}_{i}^{m}<\varepsilon^{-}\), the acquisition timeinterval will increase \(\Delta\tau\), and this judgment will be conducted periodically, i.e., if \(\left|x_{i}-x_{i}^{\prime}\right|/\overline{D}_{i}^{m}<\varepsilon^{-}\) still holds, the acquisition timeinterval will continue to increase \(\Delta\tau\); in other cases, the collection timeinterval will stay unchanged. A dynamic regulation method based on acquisition time and feedback control is shown in Figure 1.  
In Figure 1, the control index is the data changing threshold [\(\varepsilon^{-}\), \(\varepsilon^{+}\)], which is used to adjust the acquisition timeinterval; the controlled object is the collection timeinterval \(T\), which is a variable; and the control law is the associated relationship formed by data collection, computing \(\overline{D}_{i}^{m}\), predicting \(\mathrm{x_{i}}^{\prime}\), computing \(\left|x_{i}-x_{i}^{\prime}\right|/\overline{D}_{i}^{m}\), comparing [\(\varepsilon^{-}\), \(\varepsilon^{+}\)], and so on. The implementation steps for dynamically regulating the acquisition timeinterval based on feedback control are as follows. First step: Initialize system and define the size of the collection window, data changing degree threshold, and size of the acquisition time slice. Second step: Start the timer and make the timeinterval of the timer equal to the acquisition timeinterval. Third step: Conduct the data acquisition. Fourth step: Collect the data in the acquisition window and compute the variance and predict the value of the next acquisition point. Fifth step: Collect the next data point and conduct comparison with the predicted value and compute the relative changing degree. Sixth step: Judge according to the changing degree, i.e., if the changing degree is larger than the given maximum threshold, decrease the current collection timeinterval; if the changing degree is smaller than the given minimum threshold, increase the current acquisition timeinterval. Seventth step: Modify the current timeinterval of the timer and make it equal to the new acquisition timeinterval. Eighth step: Execute steps 3-7 repeatedly until the end of data collection. Figure 1: Dynamic regulation based on acquisition time and feedback control. **Remarks**: In Algorithm 1, firstly initialize the system according to the parameters, such as the size of acquisition window, data changing degree threshold, and acquisition time slice. Then, start the timer and set the initial collection timeinterval to be \(T\). Finally, enter into the data acquisition loop. In the data acquisition loop, firstly save the collected data into the dataset \(S\), then compute the variance of the data in the acquisition window according to the collected data, and then conduct the regression analysis for the data in the collection window and predict the value of the next acquisition point. According to Equation (1), judge whether the data changing degree exceeds the preset threshold. If the value of the relative changing degree is greater than the maximum data changing degree threshold, the acquisition timeinterval will be decreased by an acquisition time slice; if the value of the relative changing degree is less than the minimum data changing degree threshold, the acquisition timeinterval will be increased by an acquisition time slice; and in other circumstances, the acquisition timeinterval will be maintained.