自主空战中无人机规避导弹机动策略研究

Evasive Maneuvers Against Missiles for Unmanned Combat Aerial Vehicle in Autonomous Air Combat

针对具有一定自主空战能力的无人机,建立了以过载为输入的飞行动力学模型和采用三维比例导引法的导引弹道模型,并且结合神经网络为无人机设计了一种规避来袭导弹的机动策略。通过坐标系的变换减少了"无人机—导弹"这一复杂系统的自由度,将系统模型简化为少数变量输入和单变量输出的非线性模型。基于此模型生成并学习神经网络的样本库,并利用神经网络直接从无人机与导弹的位置关系预测规避结果,为无人机实时提供规避策略。仿真算例验证了规避算法的有效性。

For UCAV having the capability to deal with autonomous air combat, the flight dynamics model with the overload input and the 3-dimensional proportional navigation guidance model were established. Based on artificial neural network, an evasive maneuver decision was presented for avoiding incoming missiles. The degrees of freedom for UCAV-missile system were reduced by coordinate transformation, which simplified the complicated model as a non-linear model with relatively small amount of input and a single output. After the neural network samples were generated and trained, evasive results could be directly predicted from the relationship of positions between UCAV and missile through neural network, providing real-time evasive strategies to UCAV. Simulation example was also presented to verify the evasive method＇s effectiveness.