A three-dimensional intersection localization method for moving target using two vision-based UAVs (unmanned aerial vehicles), which did not rely on the distance information from the target point to the UAV was proposed. An interacting multiple model estimator was adopted to the localization method to solve the problem of not knowing the motion form of the moving target. A modified Sage-Husa adaptive filtering algorithm that synthesized the covariance matching technique and the positive definiteness judgment was used to improve the accuracy of localization. To assess the performance of these approaches, a set of simulations that carried out under realistic conditions were presented. Results show that the method proposed can get the accurate three-dimensional coordinates of the target. The modified Sage-Husa adaptive filtering algorithm can improve the localization accuracy significantly, with the average estimation error reduced from 27.13 m to 14.62 m under the intersection angle of 90°. The influence of the intersection angle on localization was studied in the simulation, which shows that too small intersection angle is not conductive to the improvement of localization accuracy, a larger intersection angle is good for the localization method without filtering, but the effect on the method with the modified Sage-Husa adaptive filtering algorithm is not significant.

Aiming at the high dynamic multi-body separation problem of hypersonic vehicle, intelligent prediction of aerodynamic characteristics and simulations of separation trajectory were carried out, which provided technical support for separation system design, separation window selection, separation scheme optimization and evaluation. Typical states were selected to carry out numerical simulations and grid force measurement in wind tunnel tests, thus an aerodynamic database was established. Embedded neural network with high and low fidelity data was used for the learning and training processes, and the structure of neural network was optimized by the genetic algorithm, leading to the error between the predicted aerodynamic coefficients and those of the grid force measurement less than 5%. The fourth-order Runge-Kutta method was used to solve the six-degree-of-freedom motion equation of aircraft, and the separation trajectory simulation method based on neural network training results was established. The Monte Carlo analysis and sensitivity analysis were carried out under different initial separation conditions to evaluate the main factors affecting separation safety. Compared with the capture trajectory system (CTS) simulation results in wind tunnel, the platform is reliable in separation trajectory prediction, and the cost is low, which can quickly improve the trajectory simulation ability and better support the separation scheme design.

With the development of aircraft, higher requirements are put forward for the accurate prediction of aerodynamic and operating characteristics under different wind tunnel simulation conditions. The flow quality of wind tunnel is one of the main factors affecting the accuracy of wind tunnel test data. A Mach 5.5 nozzle used as the research object in this paper, the non-uniformity of nozzle outlet flow field is studied. The structure of weak waves in nozzle is captured by numerical simulation. The influence of different factors on the quality of nozzle outlet flow field is analyzed, including the profile design method, profile machining accuracy and step joint at profile splicing. Combined with the comparison and analysis of the wind tunnel calibration results, it is concluded that the main factor affecting the quality of nozzle outlet flow field is the step joint at profile splicing. An improvement strategy of segment the nozzle properly is proposed to improve the quality of flow field.