# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 3 The Situation and Situational Awareness in Behavior Control Problem for UAV
In all cases where situational awareness generation is concerned, an assessment of the current situation is required, which can be accomplished either by processing the data from the relevant sensor tools or by calculation based on the first kind of data. In addition, in some tasks, the prehistory of the situation may also be required. Let us take the traditional representation of the controlled motion model in the state space as an example:  
\[ \begin{split}\dot{x}&=f(x,u),\\ y&=g(x,u),\\ u&=h(y),\end{split}  \tag{18}\]  
where \(x=(x_{1},\ldots,x_{n})\) is vector of states, \(u=(u_{1},\ldots,u_{m})\) is vector of controls, and \(y=(y_{1},\ldots,y_{r})\) is vector of observations. If the system (18) belongs to the class \(\mathbb{CS}\), then its element \(g(x,u)\) is a standard observer [93]. As a rule, the (18) must take into account the constraints on the values of the controlled object states, i.e.,  
\[x_{i}\in X_{i},\ i=1,2,\ldots,n;\ \ x\in X,\ X\subseteq X_{1}\times X_{2} \times\ldots\times X_{n}. \tag{19}\]  
A particular important case of constraints (19) is their dynamic version, in which the values of constraints depend on the current state of the UAV and on time. An example of such constraints could be a dynamically formed boundary of the region of allowed states, formed when the UAV moves in a restricted environment (urban area, a narrow and winding gorge in mountainous terrain, etc.). For the case of the \(\mathbb{AS}\) and \(\mathbb{IS}\) class systems, we can say that the formation of situational awareness is assigned to the observer version, significantly extended compared to (18). Its functions are raised because it is not only the values of state variables that need to be evaluated. These values are only a part of the internal components of the situation (there are also components of a <<diagnostic>> nature, there may also be components characterizing the operation of the aircraft design, required for active control of the structure). However, there are also external components of the situation, representing the state of the environment in which the considered UAV operates. Consequently, the advanced observer works not only with data from traditional sensors of various kinds (gyroscopes, accelerometers, speed and position sensors of the aircraft, etc.) but also with data coming from such devices as video cameras, radars, infrared cameras, range finders, ultrasonic meters, etc. Here you can see similarities with traditional feedback control methods. In these methods, the current situation is a state vector for the controlled object. Situational awareness is formed with the help of an observer (a complex of measuring and computing means), and observability of state variables is usually incomplete. Using only the estimation of the current situation to generate control signals is P-control, which is known to be ineffective. Improvement of control quality can be achieved by considering the rate of change of state variables when generating control signals by introducing derivatives for state variables into the control law. In this case, we move from P-control to PD-control. Taking into account the prehistory is the introduction of an integral component in the control law, i.e., the transition to PID control.