An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Abstract

Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.