To find a trajectory with low radar detection probability for stealth aircraft under the assumption of 2D space, performing a rapid turning maneuver is a useful way to reduce the radar detection probability of an aircraft by changing the azimuth angle rapidly to reduce the time of high radar cross-section (RCS) exposure to radar. However, in real flight, not only does the azimuth angle to the radar change rapidly but the elevation angle also changes rapidly, and the change in the radar cross-section is also significant in this process. Based on this premise, this paper established a trajectory planning method based on the sparse A* algorithm in a 3D complex, dynamic environment, called the 3D sparse A* method, based on a log-normal radar model (the 3D-SASLRM method), which considers the RCS statistical uncertainty and the statistical characteristics of the radar signals. Simulations were performed in both simple and complex scenarios. It was concluded that the established 3D-SASLRM method can significantly reduce the radar detection probability. And the essence of reducing under the assumption of 3D space is also to reduce the time of high radar cross-section exposure to radar.

The wing aerodynamic shape optimization is a typical high-dimensional problem with numerous independent design variables. Researching methods to reduce the dimensionality of optimization from the perspective of aerodynamic characteristics is necessary. One traditional aerodynamic-based approach decouples the wing’s camber and thickness according to the thin airfoil theory, but it has limitations due to unclear application scope and effectiveness. This paper proposes an improved approach that determines the values of certain thickness variables based on a data-driven aerodynamic characteristics model before optimization, which considers longitudinal stability. By reducing the number of design variables, the dimensionality of optimization is decreased effectively. The derivation of the improved approach is accomplished through the design of experiments, parametric modeling, computational fluid dynamics, and sensitivity analysis. The effectiveness of the improved approach is validated by applying it to the aerodynamic optimization of an ONERA-M6 wing in subsonic flow based on the surrogate-based optimization algorithm. The results demonstrate that the improved approach significantly accelerates the optimization process while maintaining global effectiveness.

Based on the quasi-six-degree-of-freedom flight dynamic equations, considering the changes in the elevation angle caused by an increase in the rolling angle during maneuvering turns, which leads to a rise in the radar cross-section. A computational model for the radar detection probability of aircraft in complex environments was constructed. By comprehensively considering flight parameters such as turning angle, rolling angle, Mach number, and radar power factor, this study quantitatively analyzed the influence of these factors on the radar detection probability. It revealed the variation patterns of radar detection probability under different flight conditions. The results provide theoretical support for the Radar Valley Radius and Turning Maneuver Method (RVR-TM) based on decision trees, and lay the foundation for the development of subsequent intelligent decision-making models. To further optimize the trajectory selection of aircraft in complex environments, this study combines theoretical analysis with reinforcement learning algorithms to establish an intelligent decision-making model. This model is trained using the Proximal Policy Optimization (PPO) algorithm, and through precisely defining the state space and reward functions, it accomplishes intelligent trajectory planning for stealth aircraft under radar threat scenarios.

The flush airdata sensing system (FADS) is a measurement method that calculates aircrafts airdata by obtaining the pressure distribution from the ports. The traditional design method of port layout is to install the ports on the side of aircraft nose where the pressure is sensitive to angle of sideslip and angle of attack. Yet, for the aircraft whose surface pressure gradient with respect to angle of sideslip is rather slighter than that with respect to angle of attack, especially for blended wing body configuration, its measurement errors of angle of sideslip will increase significantly. In order to enhance the measurement accuracy, this paper proposes a resolution-based port layout design method for plane aircrafts while the traditional gradient-based design method deteriorates. Firstly, the pressure distribution of aircraft under various cases are obtained by computational fluid dynamics method, and then the feasible set of ports is introduced. Secondly, the layouts under different port counts are gained by resolution-based method and traditional method respectively. Finally, it establishes FADS to predict the angle of attack, angle of sideslip and airspeed by artificial neural network, and evaluates the performance of resolution-based method and traditional method. The result shows that, the resolution-based method obtains the optimal layout, and has a quite minor error and system uncertainty than traditional method, which effectively improves the system's accuracy.

Range of insensitivity is a crucial characteristic for pitot-static probe, and it is defined as the range of aerodynamic angle where the measurement error does not exceed acceptable tolerance. This article proposes a static orifice design in order to extend the range of insensitivity for static pressure measurement. The assumption of pressure in the static pressure cavity of a multiorifice pitot-static probe is empirically proposed and verified by wind tunnel test. Based on this assumption, the circumferential distribution of orifices for ten-orifice pitot-static probe is optimized using the adaptive simulated annealing (ASA) algorithm. After optimization, within −15° to −25° angle of attack (AOA) and 0°–15° angle of sideslip (AOS), the weighted static pressure coefficient error decreases from 0.0331 to 0.0249. The range of insensitivity is reduced by 5° in AOA direction but extended by 5° in AOS direction, where the experiment shows a good acceptance with numerical results.