An intelligent decision-making algorithm based on convolutional neural networks is proposed to select the interception methods when facing a sudden attack from unmanned aerial vehicle(UAV)swarms.The theory of fuzzy mathematics is applied to assess the threat level of the UAV swarm's behavior factors.The trigger logic from the UAV swarm's behavior factors to the interception methods is established in order to generate the sample data.The structure of convolutional neural networks is designed to model the nonlinear relationship between the UAV swarm's behavior factors and the corresponding interception methods.On this basis,the model is trained and tested by using the sample data,and the performance of the proposed algorithm is compared with the Back Propagation(BP)neural network algorithm and the Support Vector Machine(SVM)classification algorithm.The simulation results show that the convolutional neural network can achieve the intelligent decision-making of the interception methods for UAV swarm and have a high accuracy.
This paper presents a classic missile-type parallel-approach guidance law for fixed-wing UAVs in coordinated formation flight. The key idea of the proposed guidance law is to drive each follower to follow the virtual target point. Considering the turning ability of each follower, the formation form adopts the semi-perfect rigid form, which does not require the vehicle positions form a rigid formation, and the orientations keep consensus. According to the mission characteristics of the follower following a leader and the leader following a route, three guidance laws for straight, turning, and circling flight are designed. A series of experiments demonstrate the proposed guidance law's improved response and maneuvering stability. The results of hardware-in-the-loop simulations and real flight tests prove that the proposed guidance law satisfies the practical UAV formation flight control demands.
To enhance the adaptability of the passive co-tracking system in complex environment and improve the tracking accuracy, an adaptive filtering algorithm based on observation noise estimation is proposed. In the proposed framework, the Sage-Husa filtering technique with an improved noise estimator was adopted in UKF algorithm. The improved Sage-Husa algorithm utilized a dynamic statistic of the residual vectors to get better estimation accuracy and stability. In addition, a matching strategy based on a self-correlation portion of the covariance was implemented, which helped prevent the filtering divergence caused by the non-positive noise variance. The experimental results showed that the proposed algorithm can improve the tracking accuracy and robustness effectively.
空射火箭在稠密大气层飞行时受载荷约束显著,且大攻角的飞行特点使得传统轨道设计中利用攻角动压积来表征载荷的方法不再适用.提出了适用于空射火箭轨道设计过程的载荷表征手段,在轨道设计模型中加入特征截面的载荷约束,使得轨道设计时对攻角剖面的调整能够准确反应飞行轨道与载荷的耦合关系,通过一体化优化快速获得满足多约束的最优轨道.推导了空射火箭动力学和运动学微分方程,给出一级飞行段典型程序攻角设计剖面,推导了参与轨道迭代设计的特征截面载荷计算公式;结合hp自适应伪谱法,使用典型算例说明本方法的有效性.研究表明,本方法可减小解耦以及设计量降维造成的可行域损失,并提高了设计效率.
面向导弹武器快速机动发射的实战化需求,开展了新一代测发控系统需求分析,提出了一种基于发射区无人值守的测发控系统设计方法.该方法具有系统搭建便捷、数据处理集中化及通用化程度高等特点.完成了测发控系统架构设计,详细描述了关键设备测发控主机的硬件架构、外部通信、供配电和软件等设计要点,分析了实现无人测发控系统的自适应数据判读、自动化流程驱动和系统健康管理三项关键技术,为导弹武器测发控系统的无人化发展提供支撑.
