In recent years, artificial intelligence has attracted research interest and developed rapidly. As one of its representative technologies, deep reinforcement learning methods have gradually combined with various fields to develop numerous research results. Aiming at the motion constraints of fixed-wing aircraft and the existence of disturbances, model uncertainties, etc., this paper designs a fixed-wing aircraft formation controller based on the PID control structure and using the Proximal Policy Optimization algorithm (PPO). The simulation results show that the PID parameters change adaptively with the state of the system, and the system can quickly form the desired formation.
Constructing an accurate battlefield situation is indispensable to accomplish the combat mission. It’s difficult to build the closed-loop battlefield combat chain in the complex battlefield environment by relying on single sensing information. Thus it has been urgent for solving the problem of how to use the available multi-sensor information to complete the task of battlefield situation construction under multi-constraint and high dynamic conditions. Based on the battlefield combat mission, this paper analyzes the filtering estimation and fusion methods in battlefield situation awareness, then summarizes and prospects the development of them considering the actual constraints that exist in battlefield situation awareness network.
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