论文标题
使用改进的多重模型机制的PN引导传入导弹的参数识别
Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism
论文作者
论文摘要
针对传入导弹的主动防御需要信息，包括指导法参数和一阶横向时间常数。为此，假设具有比例导航（PN）指导法的导弹试图用Bang-Bang Evasive Areuvers攻击空中目标，这是本文构建的基于门控复发单元（GRU）神经网络的参数识别模型。导出了指导法参数和一阶横向时间常数的分析识别解决方案。识别模型的输入可在飞机和导弹之间可用，而输出包含导弹参数的回归结果。为了提高模型的训练速度和识别精度，本文提出了一种称为改进的多人模型机制（IMMM）的输出处理方法。 IMMM的有效性和已建立模型的性能通过在各种参与方案下的数值模拟来证明。
An active defense against an incoming missile requires information of it, including a guidance law parameter and a first-order lateral time constant. To this end, assuming that a missile with a proportional navigation (PN) guidance law attempts to attack an aerial target with bang-bang evasive maneuvers, a parameter identification model based on the gated recurrent unit (GRU) neural network is built in this paper. The analytic identification solutions for the guidance law parameter and the first-order lateral time constant are derived. The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of missile parameters. To increase the training speed and the identification accuracy of the Model, an output processing method called improved multiplemodel mechanism (IMMM) is proposed in this paper. The effectiveness of IMMM and the performance of the established model are demonstrated through numerical simulations under various engagement scenarios.
