# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 5 Forming Elements of Situational Awareness for UAVs
#### 5.1.1 Reference Systems That Provide Object Tracking in a Fragment of Objective Reality
As noted above, the behavior (11) of some observable object as a dynamic system \(\mathbb{S}\) in a fragment \(\mathcal{R}^{W}\) of objective reality is a sequence of states \(x(t_{i})\in X\) of this object, tied to the corresponding time moments \(t_{i}\in T\)--i.e.,  
\[\left\{\left\langle x(t_{i}),t_{i}\right\rangle\right\},\ x(t)=\left(x_{1}(t), x_{2}(t),\ldots,x_{n}(t)\right),\ t_{i}\in[t_{0},t_{f}]\subseteq T,\ i=0,1,\ldots,N. \tag{20}\]  
The components of situational awareness about the behavior of the considered object are the results of observing it. These results are obtained in one way or another for the model (18):  
\[\left\{\left\langle y(t_{i}),t_{i}\right\rangle\right\},\ y(t)=\left(y_{1}(t), y_{2}(t),\ldots,y_{r}(t)\right),\ t_{i}\in[t_{0},\ t_{f}]\subseteq T,\ i=0,1,\ldots,N. \tag{21}\]  
The task of analyzing and predicting the behavior of some object from \(\mathcal{R}^{W}\) is to reconstruct the functional dependence describing the obtained data (21), and then use it to predict behavior for \(p\) steps ahead. We considered a corresponding example of solving the problem of aircraft behavior analysis and prediction in [51]. This example uses synthetically generated trajectoriesobtained with the FlightGear Flight Simulator [94] for the Schleicher ASK 13 glider as the source dataset. Each entry in the dataset is a collection of state vector values for a given aircraft: flight altitude; geographic coordinates (latitude and longitude); and pitch, roll, and yaw angles. An example of a part of the trajectory from the generated dataset is shown in Figure 1. The points on the trajectory are labels of geographic coordinates and altitude. For the convenience of perception, the points are connected in chronological order. The darker points correspond to the beginning of the part of the trajectory and the lighter ones to its end. The obtained values of geographic coordinates were converted into values for the Universal Transverse Mercator (UTM). In addition, absolute coordinates were converted into displacement values \(\Delta x_{k}=x_{k}-x_{(k-1)}\) at \(k\) step relative to \(k-1\) step, for all \(k\), during data preparation. The duration of each experiment was 1000 s, with intervals between measurements of 1 s. The initial flight altitude was assumed to be 5000 m, and the initial speed was 250 km/h. The data acquired in this way were used as training, validation, and test data to obtain models for analyzing and predicting the aircraft's motion in question. To solve the described problem, a relatively simple version of a recurrent neural network with a combination of LSTM blocks [95; 96; 97; 98] in combination with a fully connected layer is used [77]. The training of the constructed network was performed based on the \(x,\ y,\) and \(z\) displacements introduced above, independently for each target coordinate. We did not predict the angles responsible for the angular orientation of the aircraft. Figure 1: An example of a part of the trajectory for the generated dataset. The effectiveness of the solving of the considered problem can be evaluated by the results presented in Figure 2. It shows the model errors in predicting by variables describing the trajectory motion of the tracked aircraft. A more detailed description of this experiment and its results are presented in our article [51].