Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.     

某船载无人操控天线末段跟踪的数引策略改进

从无人操控天线需要依赖数引完成目标自动捕获以及现有的主动段航天测量的数引模型精度不够的背景出发,指出提高数引精度即进行数引改进的必要性。然后结合末段跟踪的特殊性,从时差、视差和延时累积误差三个影响数引精度的最主要方面详细分析了它们的形成机理,并给出了各自的改进方法。