Desired Impact Time Range Analysis Using a Deep Neural Network

This paper proposes a desired impact time feasible region estimation model based on a deep neural network. First, a specific multi-constraint guidance law is derived, and the terminal command deviations caused by conventional calculation methods are analyzed. Second, a binary search method is employed to determine the desired impact time range, and samples are collected under various conditions. Next, parameters related to the desired impact time range are analyzed for their sensitivity to identify their influence, thereby improving computational accuracy and reducing sample size. Finally, the accuracy of the proposed method is validated through simulations. Compared with conventional approaches, the DNN-based model demonstrates higher accuracy and provides robust support for simultaneous multi-target engagement.