Optimal Guidance Method for UCAV in Close Free Air Combat

Abstract

Aiming at the problem of Unmanned Combat Air Vehicle (UCAV) air combat decision-making and maneuver optimization, an UCAV optimal decision method based on dynamic Bayesian network (DBN) is proposed. Firstly, The DBN maneuver recognition model is established based on the causal relationship between flight characteristic parameters and maneuver actions, and the target flight path is predicted according to the acquired attitude information and trajectory prediction model. Secondly, combined with the comprehensive analysis of other information, the air combat occupation decision is established, and the decision result is the functional index of maneuver optimization to be adopted by UCAV. Finally, used optimal control algorithm to calculate the optimal boot quantity iteratively. The simulation results prove the convergence and real-time performance of the control algorithm, it can meet the requirements of engineering application.