## 2 Method
### Dataset Building  
It is necessary to measure a variety of flights that reflect the UAV maneuverability in different directions on the established platform. In automatic practical flight, the fixed-height mode is often used, and the other modes (take-off, landing, oblique acceleration, etc.) are relatively scarce. Meanwhile, the UAV has the phenomenon of jitter and tilt in the process of take-off and landing, and thus the collected data are unstable and irregular. In order to clearly describe the collected UAV flight data of different control commands under the commonly used condition, the direction of the UAV nose was defined as the positive x-axis (0\({}^{\circ}\)), and the vertical direction of the UAV nose on the right was defined as the positive y-axis (90\({}^{\circ}\)). Then, the flight trajectories in the eight horizontal directions (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\)) were selected and the corresponding flight data were collected. The original flight data have problems of fluctuation and consistency, so the data must be pre-processed and standardized. UAV data contain a large amount of variable information, such as acceleration, velocity, battery voltage, and control command values, and are widely distributed. It is easy for the data discreteness and non-uniformity to cause the convergence problem during model training. Therefore, it is necessary to preprocess these mass data.  
Firstly, the originally collected data were classified and the data that changed illogically during the flight were discarded. If a set of data has abnormal fluctuations (such as UWB data abnormality, communication errors, etc.), these data are discarded and a new set of data is collected and obtained. Secondly, the Gaussian filter was used to filter the trajectory data to ensure that the subsequent calculation of the UAV velocity is more accurate. Finally, data standardization was performed after data preprocessing. Before the model training, the data standardization takes into account the weight initialization, which makes the data calculation easier in the initial training stage. After the model training, the output of the actual constructed algorithm model should be converted to the normalized values, and then the corresponding control instructions are obtained [23]. After Z-SCORE standardization of the flight data, the data conform to the standard normal distribution. The mean is 0 and the standard deviation is 1. This standardized method can play an important role when the data distribution is uneven and the situation is not clear. The conversion formula can be expressed as  
\[Y(x)=\frac{x-\bar{x}}{\sigma} \tag{4}\]  
Figure 2: Block diagram of the real-time control and acquisition system.  
where \(x\) is the initial value, \(\hat{x}\) is the average value of all of the elements, and \(\sigma\) is the standard deviation of all of the elements.  
After the original data were selected, filtered, and standardized, a relatively complete flight database was formed. The database contains the data in eight flight directions, and tens of thousands of pieces were obtained as the dataset for the maneuverability model. Each piece of data contains five state variables (x-axis acceleration, y-axis acceleration, x-axis velocity, y-axis velocity, and battery voltage) and four control variables, which include the left-right control instruction (ROLL), the front-rear control instruction (PIT), the spin control instruction (YAW), and the throttle control instruction (THR).