## 3 Research Environment and Plan of Experiment  
### Flight Simulation Environment and Data Acquisition  
A simulator stand (Figure 8) was used to assess the possibility of estimating the value of gusts with the use of the method presented in Section 2. Figure 8: Stationary simulator station. Figure 6: Cascaded complementary filter diagram. Figure 7: Bode diagram (amplitude and phase) for cascaded complementary filter. The block diagram of the stand is presented in Figure 9.  
The flight simulation was made using the X-Plane 11 software version certified for professional use. For research purposes, layers of programmatically generated atmospheric gusts were introduced in the simulation environment. The flight parameters of one of the most popular medium-range narrow-body aircraft (B737-800) were recorded. The simulation was performed using a realistic model of this type of aircraft (Figure 10), which very accurately reflects its dynamic properties and operational functions. The analysed estimation algorithms assume the use of data available from the following on-board systems: ADIRU, GPS and flight controls systems. A detailed summary of the recorded flight parameters, taking into account potentially available physical data sources, is presented in Table 1.  
Figure 10: (**A**) External view of a jet airliner—Boeing 737–800—during simulation; (**B**) cockpit view of a jet airliner—Boeing 737–800. Figure 9: Block diagram of the stand for flight simulation and data acquisition. Due to the need to record flight parameters which are not available through the built-in user interface and require reference to the internal variables of the X-Plane (Datarefs), the X-Plane Communication Toolbox (XPC) was used [28]. This toolbox was launched and configured in the Matlab/Simulink environment to read internal X-Plane 11 data via UDP protocol (Figure 9). The simulations were carried out with the following assumptions:  
* Aircraft weight: 65,000 [kg];
* Constant thrust--descent at idle thrust;
* Automatic flight while crossing gust area;
* Autopilot set at constant IAS = 270 kt;
* Constant headwind direction--without crosswind and vertical gusts;
* Simulation performed by pilot with current Type Rating on B737. A complete diagram of the data flow in the simulation as well as in the wind parameter estimation process is shown in Figure 11. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Source** & **Symbol—Unit** & **Sampling Frequency** \\ \hline Time & Simulation time & t [s] & 12.5 Hz \\ Altitude & Cockpit indicator & ALT [m] & 12.5 Hz \\ Indicated air speed & Cockpit indicator & IAS [m/s] & 12.5 Hz \\ True air speed & Cockpit indicator & TAS [m/s] & 12.5 Hz \\ Ground speed & Flight model position & GS\_x [m/s] & 12.5 Hz \\ Wind speed x (OGL) & Simulation weather & Wind\_x [m/s] & 12.5 Hz \\ Wind speed y (OGL) & Simulation weather & Wind\_y [m/s] & 12.5 Hz \\ Wind speed z (OGL) & Simulation weather & Wind\_z [m/s] & 12.5 Hz \\ Pitch rate & Flight model position & Q [deg/s] & 12.5 Hz \\ Pitch angular & Flight model position & Q\_dot [deg/s\({}^{2}\)] & 12.5 Hz \\ acceleration & & & \\ Horizontal Stabilizer & Flight model controls & HS\_elev [deg] & 12.5 Hz \\ elevator deflection & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters recorded in the X-Plane environment for the purposes of testing the headwind gust estimation algorithm. Figure 11: Flight simulation and wind parameter estimation—data flow scheme. According to this scheme, X-Plane 11 Pro simulator feeds the Matlab/Simulink environment with two types of data:  
* Information from simulated on-board systems (cockpit indicators, flight model position, and flight controls);
* Data on the current parameters of the wind generated by the simulator environment (simulation weather). The X-Plane 11 Pro software uses a complex non-linear model of the aircraft dynamics.