A Cooperative Interception Allocation Method Faced on 3D Reachable Area Prediction Based on Deep Neural Network

This paper studies the problem of multi-intercept missile cooperative interception allocation faced on three-dimensional reachable area prediction. The key points of this paper are using neural network to predict the three-dimensional reachable area fastly, and designing a cooperative interception allocation method faced on the three-dimensional reachable. This paper introduces the height information into the traditional two-dimensional reachable area model to solve the problem of insufficient prior information of the target, and designs a fast prediction model to solve the problem of time-consuming in traditional reachable area prediction based on deep neural network. Furthermore, this paper proposes an allocation method of multi-interceptor missile cooperative interception to solve the problem of low interception efficiency of single interceptor missile. As a result, this paper designs a multi-intercept missile cooperative interception allocation method faced on reachable area prediction based on deep neural network. The simulation results show that the proposed method can quickly fit the three-dimensional area of the hypersonic vehicle, and the multi-interceptor coverage interception allocation model has a high coverage of the reachable area of the aircraft.