Event-Triggered Cooperative Guidance for Salvo Attack With Impact Vector and Field-of-View Constraints

In this article, we investigate the 3-D cooperative salvo problem under field-of-view (FOV) constraints and without active speed control. An impact vector control guidance (IVCG) law is proposed by introducing a unified auxiliary variable that integrates the lead angle and the 3-D impact angle error, ensuring their simultaneous convergence. To improve time-to-go estimation performance, a physics-guided neural network (PGNN) is developed to provide high-precision prediction with reduced online computational demand. Furthermore, two sequential bias terms are incorporated into the IVCG law to achieve simultaneous arrival with reduced communication frequency while strictly satisfying the FOV constraint. The main advantage of the proposed approach is that it handles multiconstraint guidance for varying-speed missiles, with better time-to-go prediction than deep neural network and physical models, and lower computational and communication costs than numerical integration and sampled communication. Numerical simulations demonstrate the effectiveness and comparative performance of the proposed cooperative guidance law.