A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network

In this paper, a hybrid deep learning network-based model is proposed and implemented for maneuver decision-making in an air combat environment. The model consists of stacked sparse auto-encoder network for dimensionality reduction of high-dimensional, dynamic time series combat-related data and long short-term memory network for capturing the quantitative relationship between maneuver control variables and the time series combat-related data after dimensionality reduction. This model features: using time series data as the basis of decision-making, which is more in line with the actual decision-making process; using stacked sparse auto-encoder network to reduce the dimension of time series data to predict the result more accurately; in addition, taking the maneuver control variables as the output to control the maneuver, which makes the maneuver process more flexible. The relevant experiments have demonstrated that the proposed model can effectively improve the prediction accuracy and convergence rate in the prediction of maneuver control variables.