This paper addresses the problem of task execution for unmanned aerial vehicles (UAV) swarms, considering the coupling characteristics of UAV task allocation and trajectory planning as well as the no-fly zone constraints. A task planning algorithm is proposed that can make the UAV swarm reach the target positions in the shortest time simultaneously. A "hovering waiting and dynamic speed adjustment" method is used to synchronize the arrival time of each UAV, Dubins curves are used to design the pathways, and an upgraded particle swarm optimization (PSO) algorithm with particle swarm mutation is used to optimize the task allocation scheme. Finally, the effectiveness of the algorithm is evaluated and verified in a simulation environment based on the six-degree-of-freedom dynamics model and the dynamic inverse control model. In contrast to the conventional PSO algorithm approach, the simulation results demonstrate that this enhanced PSO algorithm is capable of successfully escaping the local optimum and achieving a better allocation scheme. Under the control of the proposed algorithm, the maximum deviation of flight time among multiple UAVs is only 0.5%, meeting the requirements of a saturation attack.

The deep integration of wind tunnel experiments, numerical calculations, and model flight tests is an inevitable requirement for developing future high-speed vehicle. This article focuses on constructing high-speed wind tunnel facilities, numerical calculation software and hardware, model flight testing capabilities, and self-developed flight test techniques for measuring the surface temperature, heat flux, pressure, friction drag, etc. Based on the characteristics of aerodynamic data fusion, an aerodynamic data fusion method, based on the correlation between aerodynamic data and physical models as well as deep learning, was developed to solve the problem of data association between different data sources, significantly improving the data's reliability. The data fusion method has been successfully applied in the flight-ground correlation of the pitching moment coefficient and aerothermodynamic data for high-speed vehicle. A comprehensive study of the high-speed shock/boundary-layer interaction problem using various methods results in an empirical formula of the interaction force/thermal load, a correction to the pressure/heat flow correlation, and the verification of the low-frequency oscillation of the separation bubble under real flight conditions.

Due to the correlation between flight and ground test data, there is a certain difference between the wind tunnel test data and the flight test data. Limited by the flight trajectory and test excitation, the flight test data distributes in a small characteristic sections of the flight trajectory. And due to the limitation of ground simulation capabilities, wind tunnel tests have certain differences between the test data and the actual flight data. In order to remedy these limitations, researches of the data fusion algorithm based on above two different sources of aerodynamic data are conducted. Firstly, through the aerodynamic parameter identification method, the six-component real aerodynamic data of the aircraft along the actual flight trajectory are obtained, and then compare the difference and consistency with the wind tunnel test data. Finally, two kinds of data fusion methods based on gradient information and Gaussian process regression were used to fuse the two sources of aerodynamic datas. The results show that both the prediction data of the two fusion models are more accurate than the single source model; if the gradient information of the high and low precision data is more consistent, the data fusion method based on the gradient information is more effective; while the data fusion method based on the Gaussian process regression could obtain the confidence interval of the fusion data, which is useful to the analysis of uncertainty.

Wind-tunnel experiments, numerical simulations, and flight tests are the three major means for aircraft aerodynamic research. However, each method has its limitation, thus it is difficult to accurately predict aerodynamic characteristics of an aircraft in its full flight envelope by a single approach. Data fusion is important for improving accuracy and enhancing forecasting capabilities. In the parameter space, the fused data could be a supplement to high-fidelity data. Meanwhile, the fused data outside the parameter space can provide a reference for the variation trend of the data. Therefore, aerodynamic data obtained by different methods need to be fused. To this end, two data fusion algorithms are proposed. One is a weighted fusion algorithm based on uncertainty, which uses the Gaussian process regression to obtain the characteristics of aerodynamic data from different sources before performing a weighted fusion. The other is an agent fusion model which is established by using the CoKriging algorithm. Aerodynamic data of an aircraft are taken for comparative analyses. Results show that the prediction accuracy of the Kriging model using single-precision data can be improved by more samples. Both fusion algorithms have significant higher accuracy than the Kriging model using only single-precision data. But the accuracy of the CoKriging algorithm is roughly one order of magnitude higher than the fusion algorithm based on uncertainty.