To meet the requirement for simultaneousness of multiple anti-ship missiles in attacking a ship target, this paper investigates the time-cooperative guidance problem with the event-triggered control method by considering the seeker’s field-of-view constraint. A three-dimensional time-cooperative guidance law consisting of the three-dimensional proportional navigation guidance law and an event-triggered bias term is proposed based on the three-dimensional coupled nonlinear model, and the event- event-triggering conditions are also provided. The bias term utilizes a nonlinear function to ensure that the velocity lead angle is bounded to satisfy the seeker’s field-of-view constraint and enables the impact times of multiple missiles can synchronize by means of a distributed event-triggered consensus protocol, thus reducing the frequency of cooperative control system updates and communication resource consumption. Using the Lyapunov theory, the convergence of the proposed distributed cooperative guidance law is demonstrated to have no Zeno behavior. Finally, numerical simulations are conducted to verify the effectiveness and robustness of the proposed three-dimensional cooperative guidance law.

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.